Exploring Pharmacokinetics, Genetics, and Predictive Models

The intricacies of therapeutical management have brought to light a pervasive issue – drug-related problems (DRP). A substantial portion of DRPs stems from drug prescribing challenges. These can span from inaccurate dosing to frequency mismanagement, exacerbating known allergies, or triggering drug-drug interactions (DDIs). The prevalence of polypharmacy, particularly in geriatric patients, adds another layer of complexity. Swiss studies underscore that polypharmacy prevalence increases significantly with age. Deciphering the nuances of DDIs becomes imperative, as they contribute to overall hospital admissions in the elderly population, with a notable percentage of adverse drug reactions (ADRs) being DDI-related.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions

The realm of drug interactions encompasses both pharmacokinetic (PK) and pharmacodynamic (PD) aspects. PK DDIs manifest when a perpetrator drug influences the absorption, distribution, metabolism, or elimination (ADME) of a victim drug. Conversely, PD interactions occur when two drugs directly interact on the same target without altering ADME parameters. The consequences of DDIs can range from mild side effects to severe, life-threatening events or syndromes

Drug Metabolism and Transport

Drug metabolism unfolds through phase I and phase II reactions, primarily mediated by Cytochrome P-450 (CYP450) enzymes. Phase II reactions involve conjugation processes facilitated by enzymes like UDP-glucuronosyltransferases (UGTs). Simultaneously, membrane transporters play a pivotal role in drug absorption, distribution, and elimination. Uptake transporters (OATPs, OATs, OCTs) and efflux transporters (P-gp, BCRP) are key players in influencing drug efficacy

Genetic Polymorphism of Drug Metabolizing Enzymes and Transporters

Understanding the genetic basis of drug metabolism is crucial. CYP enzymes, especially CYP1A, CYP2C-D-E, and CYP3A, exhibit significant polymorphism, influencing drug metabolism. Genetic variations contribute to 20–30% of the variability in drug response. Enzymes like UGTs and various transporters, such as P-gp and BCRP, also exhibit genetic polymorphism, adding another layer of complexity.

Predictive Models

The evolution of computational tools has paved the way for predictive models in clinical pharmacology. Quantitative structure-activity relationships (QSAR), quantitative systems pharmacology (QSP), and pharmacokinetic modeling (PK modeling) offer insights into drug behavior. A notable advancement is physiologically based pharmacokinetic (PBPK) modeling, providing a dynamic, bottom-up approach that considers individual variability and factors like genetics, diseases, and co-treatments

System Property Building to PBPK Models

PBPK models, endorsed by regulatory authorities, present a mechanistic framework for predicting drug exposure and response across diverse populations. Initiatives like PK-Sim and platforms like SimCYP® and GastroPlus® have democratized the application of PBPK models.

The Persistent Challenge

Despite strides in understanding drug metabolism and disposition, DDIs remain a challenge. Predicting vulnerability to DDIs is paramount for averting adverse effects. A knowledge base, collating PBPK predictions from literature, underscores the need for continued investigation into the interplay between DDIs and genetic variations in metabolic enzymes and transporters. The quest for a predictive model that encompasses intrinsic and extrinsic factors persists, providing a holistic approach to personalized medicine and safer drug administration.

Study DOI: 10.3389/fphar.2021.708299

Engr. Dex Marco Tiu Guibelondo, B.Sc. Pharm, R.Ph., B.Sc. CpE

Editor-in-Chief, PharmaFEATURES