Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Accurate prediction of maximum recommended therapeutic dose (MRTD) is crucial for drug safety.
• Understanding protein-drug interactions and drug promiscuity is key to predicting therapeutic outcomes.
• Previous work established methods for computational estimation of drug promiscuity and potency.

Purpose of the Study:

• To develop a simple model for predicting the MRTD of small molecule drugs.
• To leverage computational estimations of drug promiscuity and potency for MRTD prediction.
• To identify potential high-risk off-targets (HROTs) associated with low-dose drugs.

Main Methods:

• A linear model was constructed using data from 238 small molecular drugs.
• The model incorporates assessments of likely protein-drug interactions, drug promiscuity, and potency.
• The model was validated by predicting MRTDs for nonsteroidal antiinflammatory drugs (NSAIDs) and antiretroviral drugs.

Main Results:

• The developed model successfully predicted MRTDs for tested NSAIDs and antiretroviral drugs.
• 83 proteins were identified as high-risk off-targets (HROTs) frequently associated with low drug doses.
• The model provides insights into MRTD variations, particularly for drugs with severe adverse reactions linked to HROTs.

Conclusions:

• A straightforward computational model can predict drug MRTD based on protein-drug interactions.
• The identification of HROTs offers a valuable tool for early-stage drug discovery and safety assessment.
• This model aids in understanding the mechanisms behind severe adverse drug reactions related to off-target interactions.