Each participant received a single oral dose of harmine HCl (100mg, 200mg, 300mg, or 500mg) on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.

Using the CRM to inform the dose to be given to the next participant based on prior information. The CRM used a one-parameter logistic regression model to estimate the relationship between dose and dose-limiting toxicity (DLT) risk to inform decisions on dosing with a target DLT rate of 25%. It first starts with a selected target DLT rate and a mathematical model for the relationship between dose and toxicity, the prior dose-toxicity curve. After a participant experiences a DLT or not, the dose-toxicity curve is re-fit incorporating the latest outcome. At every step, the next patient is assigned the dose estimated to be nearest to the MTD. To minimize the exposure of participants to potentially toxic doses, we employed some modifications to the CRM, including starting with the lowest dose available and not skipping dose levels when escalating. Additionally, we included an early stopping rule that the study would end when either the maximum number of allowable patients treated per protocol was reached or would end early in the event that the probability of the next 10 patients being given the same dose level exceeds 90%, regardless of DLT outcomes observed.

Each participant received a single oral dose of harmine HCl on the treatment day and was observed for 8 h with continuous medical monitoring and followed up at 24 h. Note that doses >500 mg were not used due to the stopping rule above.