Ionization constants (pKa) in pharmaceutical chemistry: Experimental and computational determination in drug discovery and development

Abstract

The ionization constant (pKa) is a major factor in the determination of the chemical and physical properties of drug molecules, which, in turn, affect their distribution, absorption and interaction with the biological targets as well as their pharmacokinetic fate. The present review is an attempt to shed light on the pKa significance in the contemporary drug discovery and medicinal chemistry, pointing its use in rational molecular design and lead optimization. This paper critiques both the experimental and computed methods of pKa determination. It focuses on the key analytical techniques, particularly, UV-visible spectroscopy and NMR spectroscopy. Furthermore, it reviews the recent advances in the in silico prediction platforms and software that are used for high-throughput pKa estimation in the modern screening pipeline. The exponentially increasing number of chemical libraries for screening has made the computational pKa prediction methods indispensable, especially in the early drug discovery phase, where they offer scalable and cost-efficient solutions. It is the accuracy of the experimental validation that still plays a significant role, particularly when it comes to structurally complex molecules. The NMR spectroscopy adds unique advantages to the resolution of individual ionization states in the multi-functional systems, as demonstrated by the case of aminoglycoside antibiotics. The pKa accurate determination and prediction is the basis for drug-like properties optimization and for the compounds' smooth transition throughout the stages of discovery and development. The combination of computational and experimental approaches results in the most reliable ionization profiles. The understanding of pKa behavior allows medicinal chemists to create more effective and less toxic drug candidates by the optimization of solubility, permeability, and target interactions. The use of advanced analytical and computational tools supports efficient compound triaging, accelerating the development of therapeutically relevant molecules.