Estimation of Pharmacokinetic Parameters Using Computational Tools

A potent molecule should reach its target in the body in an adequate level and remain there in a medicinally responsive form long enough for the anticipated biological reactions to take place for it to be an effective medicine. Drug development includes evaluating absorption, distribution, metabolism, and excretion (ADME) during the discovery phase when there are many compounds under consideration but minimal access to physical samples. This is because, rather than being ineffective, ADME problems account for the majority of clinical trial failures. Hence, computational models are legitimate substitutes for experimentation. In this chapter, we present various computational tools that provide predictive models for pharmacokinetics, physicochemical properties, drug-likeness, and medicinal chemistry friendliness models such as SwissADME, QSAR ADMETlab2.0, and PK CSM and AI-based Physiologically Based Pharmacokinetics (PBPK) models are described using examples, providing an exhaustive review of all the tools available. Easy and efficient input and interpretation are ensured thanks to the user-friendly interface of these servers. Specialists, but also nonexperts in cheminformatics or computational chemistry, can rapidly predict key parameters for a collection of molecules to support their drug discovery endeavours.