Over the last several decades, the pharmaceutical industry has become proficient at optimizing the PK of small molecule leads, but still, a significant percentage of compounds fail at the end of phase II clinical trials due to safety issues or lack of efficacy.  Such high attrition is partly due to the translatability of preclinical models and underlying target mechanisms used to select candidates to progress into clinical trials, but misassignment of the optimal PK driver for therapeutic efficacy can also be a contributing factor.  In our previous work, we demonstrated that the optimal PK driver for efficacy is highly dependent on the specific biology downstream from the modulated target.  Such dependency has far reaching consequences to many critical discovery questions, ranging from: (a) whether a target can be reasonably engaged to achieve the desired clinical outcome, (b) which among the targets in the identified pathway or possible interventional modalities would most likely lead to the desired therapeutic outcome, (c) which hit series should be prioritized as the most progressible, with the highest probability of success (PoS) to achieve  efficacy at the desired dose, and (d) how to most efficiently enumerate focused virtual screening libraries and guide proposed design ideas based on the combination of potency and ADME properties specific to the desired target pharmacology.  The model-based target pharmacology assessment, mTPA, is a computational approach combining larger-scale PBPK/PD modeling, simulation, and machine learning to identify the PK driver and associated ADME-potency space needed to increase the PoS for a compound becoming a drug for the specific target-indication pair.  In the present work, we will present several cases demonstrating how mTPA has been applied to help answer these discovery questions and its potential in reducing discovery attrition.

Applications of the model-based target pharmacology assessment (mTPA) in defining drug design and DMPK strategies: GSK Experiences

ACS Fall 2021

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