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Rakesh GollenRakesh Gollen is currently pursuing his Ph.D. from Long Island University, with a major in Drug Metabolism and Pharmacokinetics, under the supervision of Dr. David Taft. His research area focus is on the predictions of pharmacokinetic parameters in special population, using the physiologically-based pharmacokinetics modeling approach.

Recently, various empirical and semi-empirical models embedded in different modeling tools have been developed and recognized for their role in predicting pharmacokinetics of drugs in humans. These models have also been used to evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure at all stages of the drug development process. Apart from shortening the time for bringing the drug to market, and minimizing the risk of failure, various physiologically-based pharmacokinetic (PBPK) modeling and simulation tools have gained popularity in the pharmaceutical industry and the Food and Drug Administration.A recent article on Predictive Modeling in Material Science, Drug Product, and Process Development by John Strong and Mary End in the Physical Pharmacy and Biopharmaceutics section’s cover article of the January 2014 AAPS Newsmagazine,  elaborates on the importance of predictive modeling in material science and drug development very well. Here I want to emphasize the role of these predictive models in other parts of drug development as a link to approach clinical predictions from the available or minimum preclinical data.

Other than classical PK models, the PBPK modeling tool is more useful due to the inclusion of specific compartments for tissues involved in drug toxicity, exposure, biotransformation, and clearance processes, that are connected through the blood flow to these compartments. Here the blood flow and different compartments are described by known physiological parameters among different species, resulting in easy extrapolation among species, and finally to humans. The mechanistic aspects of the PBPK models are supported by the integration of different biopharmaceutical, physiological, and physiochemical variables, which result in absorption, distribution, metabolism, and excretion properties of drug compounds. This includes the effect of gastrointestinal (GI) emptying time, GI blood flow, precipitation time, and stomach and intestinal pH on drug absorption, tissue volume, composition, and partition coefficient on drug distribution, expression level of various transporters and enzymes involved in drug metabolism, and glomerular filtration rate—the transporter expression level in the kidneys on the elimination of drugs.

The application of predictive modeling, with emphasis on PBPK, has been well recognized in the pharmaceutical industry for accelerating the process of drug development at different stages, including special populations, e.g.,  predictions of PK during pregnancy. This approach also allows the ADME data generated from preclinical species as input to predict the human PK profile of the drug. And these models also help to understand and analyze the difference in species, uncertainties, and variability in PK among different populations, including patients with personalized need, to fill the unmet need in medication. For the future of these predictive models, we should think of incorporating the pharmacogenomics, along with computational chemistry and high-throughput screening, into these PBPK models, which will help us save more time and resources.

Do you use a PBPK modeling approach at your workplace, and does it help in designing future studies and decision making? Please feel free to discuss your experiences and recommendations.