Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

Abstract

The occurrence of drug-drug interactions (DDIs) can significantly affect the safety of a patient, and thus assessing DDI risk is important. Recently, physiologically based pharmacokinetic (PBPK) modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling concept and strategy. We also surveyed PBPK-related articles about the prediction of DDI potential in humans published up to October 10, 2017. We identified 107 articles, including 105 drugs that fit our criteria, with a gradual increase in the number of articles per year. Studies on antineoplastic and immunomodulatory drugs (26.7%) contributed the most to published PBPK models, followed by cardiovascular (20.0%) and anti-infective (17.1%) drugs. Models for specific products such as herbal products, therapeutic protein drugs, and antibody-drug conjugates were also described. Most PBPK models were used to simulate cytochrome P450 (CYP)-mediated DDIs (74 drugs, of which 85.1% were CYP3A4-mediated), whereas some focused on transporter-mediated DDIs (15 drugs) or a combination of CYP and transporter-mediated DDIs (16 drugs). Full PBPK, first-order absorption modules and Simcyp software were predominantly used for modeling. Recently, DDI predictions associated with genetic polymorphisms, special populations, or both have increased. The 107 published articles reasonably predicted the DDI potentials, but further studies of physiological properties and harmonization of in vitro experimental designs are required to extend the application scope, and improvement of DDI predictions using PBPK modeling will be possible in the future.

Gut Reactions: Breaking Down Xenobiotic–Microbiome Interactions

“…There are also advantages to the use of in silico models to predict outcome from a variety of inputs, possibly by combining microbiota metabolism rates with host drug absorption and metabolic rates. Specifically adapting many of the emerging physiologically based pharmacokinetic models, which predict drug levels in tissues based on a variety of drug and host parameters (Min and Bae, 2017;Thiele et al, 2017;Donovan et al, 2018), to allow for greater contribution of microbiota-mediated metabolism, may be a useful approach to predict overall clinical impact.…”

Comparing potential drug-drug interactions in veterinary medications using two electronic databases

“…We compared the result from Micromedex between the drug list used for metabolic syndrome in human and animal diseases in identity contraindicated and major potential DDIs. The drug list for animal disease treatment identi ed more pairs of contraindicated and major potential DDIs; the reason might be that the drugs used in animals included many drugs related to antiarrhythmic agents, antimicrobials and antihypertensive drugs, which often show a high incidence of potential DDI when prescribed with other drugs [16][17][18]. Veterinary pharmacists should realise when prescribing these drug groups to avoid the severe adverse reactions.…”

Probenecid‐Boosted Tenofovir: A Physiologically‐Based Pharmacokinetic Model‐Informed Strategy for On‐Demand HIV Preexposure Prophylaxis

“…They have been used commonly in predicting both the impact of drug inhibition on substrate pharmacokinetics and informing clinical study designs. 5,6 In some cases, PBPK models are used in lieu of clinical pharmacokinetic data for dosing recommendations and product labeling. 6 Traditionally, DDI PBPK models were developed for enzyme-mediated interactions, but as our understanding of transporter-mediated drug disposition advances, transporter-mediated models are also being studied.…”

“…6 Traditionally, DDI PBPK models were developed for enzyme-mediated interactions, but as our understanding of transporter-mediated drug disposition advances, transporter-mediated models are also being studied. 5 In this proposal, a PBPK model was developed to predict the effects of the transporter-mediated DDI between PRO and TFV for a single-dose HIV PrEP regimen. In this model-informed strategy for on-demand HIV PrEP, a PRO-boosted TFV PBPK model was used to prospectively optimize PRO dosing for a clinical study design based on in vitro TFV and PRO parameters.…”