MATHEMATICAL MODELS IN DRUG TARGET ANALYSIS: PHARMACOKINETIC AND PHARMACODYNAMIC APPROACH
Surendra Prakash Gupta · 2023
Accurate prediction of drug targets is essential for successful drug design and optimization in the drug discovery and development realm. Various mathematical models have been developed to aid in drug target prediction, incorporating different data types and techniques. One approach is to identify and quantify the protein pathway that is important to the development of diseases or affected by drug therapy with proteome data. This approach involves the construction of quantitative system pharmaceutical models of a disease scale that can predict the therapeutic or side effects of drugs. Another approach to drug target prediction is the pharmacokinetic and pharmacodynamic (PK/PD) profiling. The PK/PD approach involves the study of how drugs interact with their target proteins and the subsequent effects on pharmacokinetics and pharmacodynamics. By understanding the thermodynamic and kinetic information of drug- target interactions, researchers can gain insights into how drugs bind to their targets and how to optimize their efficacy and minimize potential side effects. These models rely on the integration of genomic, proteomic, and metabolomic data, allowing for a more comprehensive understanding of disease pathology and drug response. By incorporating large-scale data sets and using mathematical algorithms, these models can identify key mechanisms underlying disease pathology and predict potential therapeutic targets. Additionally, mathematical algorithms can also be used to predict the "drug target-likeness" of a protein. Furthermore, mathematical models can aid in predicting the binding affinity between drugs and their targets.