Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (Kp) of drugs across different tissues

In drug discovery, estimating the drug candidate's pharmacokinetic (PK) parameters is crucial for determining its safety and efficacy within the body. The tissue-to-plasma partition coefficient (Kp) indicates how a drug partitions within a tissue, potentially leading to tissue-specific activity or toxicity. Therefore, determining K_p values for a drug is essential for its safety assessment. However, only a limited number of such studies are available. Here, we developed machine learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) models to predict the K_p values for drugs across 11 different tissues. Initially, we developed models to predict K_p values for drugs with missing K_p values for specific tissues within the dataset solely based on the structural and physicochemical properties of the drugs. Subsequently, another set of models was developed using both structural and physicochemical properties and the K_p values from other tissues. In this case, predicted values from the initial models were also incorporated where experimental K_p values were unavailable. These models demonstrate significant improvement in predictability (Q²_F1 = 0.79–0.95, Q²_F2 = 0.78–0.95) for a drug compared to the initial models. Here, we conducted a screening using a true external dataset from the SIDER database. This analysis indicates that compounds with higher tissue partitioning are more likely to exhibit toxicity to that specific tissue. Finally, a Python-based tool (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/kp-calculator) was created to predict K_p values for drugs in different tissues.