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bRifampin is a potent inducer of cytochrome P450 (CYP) enzymes and transporters. Drug-drug interactions during tuberculosis treatment are common. Induction by rifapentine and rifabutin is understudied. Rifampin and rifabutin significantly induced CYP3A4 (80-fold and 20-fold, respectively) in primary human hepatocytes. The induction was concentration dependent. Rifapentine induced CYP3A4 in hepatocytes from 3 of 6 donors. Data were also generated for ABCB1, ABCC1, ABCC2, organic aniontransporting polypeptide 1B1 (OATP1B1), and OATP1B3. This work serves as a basis for further study of the extent to which rifamycins induce key metabolism and transporter genes.T uberculosis is a major global health problem (1). Effective short-course therapy lasts for 6 months but requires rifampin, and clinically significant drug-drug interactions are common due to induction of cytochrome P450 3A4 (CYP3A4) and key drug transporters included herein (1-3). Hence, antiretroviral coadministration with tuberculosis treatment is particularly challenging. Besides CYP3A4, drug transporters can significantly alter the absorption and distribution of drugs. Many compounds used in human immunodeficiency virus treatment, particularly the protease inhibitors and nucleoside reverse transcriptase inhibitors, are transported by proteins such as ABCB1 (3), ABCC2 (4), organic anion-transporting polypeptide 1B1 (OATP1B1), and OATP1B3 (5, 6). The genes encoding these proteins are influential in the safety, efficacy, and disposition of many drugs. For example, the induction of ABCB1 by rifampin decreases the area under the curve (AUC) of efavirenz by 22% (2). Rifabutin is considered a less-potent inducer and is often used in place of rifampin for patients receiving antiretroviral drugs for human immunodeficiency virus to reduce the risk of drug interactions (7-9). The substitution of rifapentine for rifampin may reduce the treatment duration required for cure, but the induction potential of rifapentine is comparatively understudied (2, 10). The sterilizing activity of rifapentine is dose dependent in an established mouse model of tuberculosis, with eradication possible in 3 months or less when high-dose rifapentine is substituted for rifampin in a multidrug treatment regimen (11,12). However, dose increases resulted in less-than-dose-proportional increases in rifapentine exposures (10, 13). In addition, the mean area under the concentration-time curve of oral midazolam, a CYP3A4 probe, decreased by 75% when coadministered with rifampin, compared to 92% when coadministered with rifapentine, each given at 10 mg/kg of body weight daily (14). We evaluated the in vitro induction of CYP3A4 and transporters by rifampin, rifabutin, and rifapentine in primary human hepatocyte samples from six donors. Other studies have previously investigated the induction of CYP activity by rifampin, rifabutin, and rifapentine (9) and the mRNA expression of drug transporters induced by rifampin (3, 6), but no studies have comprehensively compared the mRNA induction of CYPs and ...

Prior to clinical development, a comprehensive pharmacokinetic characterization of a novel drug is required to understand its exposure at the site of action and elimination. Accordingly, in vitro assays and animal pharmacokinetic studies are regularly employed to predict drug exposure in humans, which is often costly and time-consuming. For this reason, the prediction of human pharmacokinetics at the point of design would be of high value for drug discovery. Therefore, we have established a comprehensive data curation protocol that enables machine learning evaluation of 12 human in vivo pharmacokinetic parameters using only chemical structure information and available doses for 1001 unique compounds. These machine learning models were thoroughly investigated and validated using both an independent hold-out test set and AstraZeneca clinical data. In addition, the availability of preclinical predictions for a subset of internal clinical candidates allowed us to compare our in silico approach with state-of-the-art pharmacokinetic predictions. Based on this evaluation, three fit-for-purpose models for AUC PO (R test 2 = 0.63; RMSEtest = 0.76), C max PO (R test 2 = 0.68; RMSEtest = 0.62), and Vdss IV (R test 2 = 0.47; RMSEtest = 0.50) were identified. Based on the findings, our machine learning models have considerable potential for practical applications in drug discovery, such as influencing decision-making in drug discovery projects and progression of drug candidates toward the clinic.

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration–time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R 2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R 2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

The use of in vitro in vivo extrapolation (IVIVE) from human hepatocyte (HH) and human liver microsome (HLM) stability assays is a widely accepted predictive methodology for human metabolic clearance (CLmet). However, a systematic underprediction of CLmet from both matrices appears universally apparent, which can be corrected for via an empirical regression offset. Following physiological scaling, intrinsic clearance (CLint) for compounds metabolised via the same enzymatic pathway should be equivalent for both matrices.Compounds demonstrating significantly higher HLM CLint relative to HH CLint have been encountered, posing questions on how to predict CLmet for such compounds. Here, we determined the HLM:HH CLint ratio for 140 marketed drugs/compounds and compared this ratio as a function of physiochemical properties and drug metabolism enzyme dependence; and examined methodologies to predict CLmet from both matrices. The majority (78%) of compounds displaying a high HLM:HH CLint ratio were CYP3A substrates. Using HH CLint for CYP3A substrates, the current IVIVE regression offset approach remains an appropriate strategy to predict CLmet (% compounds over-/correctly/under-predicted 27/62/11, respectively). However, using the same approach for HLM significantly overpredicts CLmet for CYP3A substrates (% compounds over-/correctly/under-predicted 56/33/11, respectively), highlighting a different IVIVE offset is required for CYP3A substrates using HLM. This work furthers the understanding of compound properties associated with a disproportionately high HLM:HH CLint ratio and outlines a successful IVIVE approach for such compounds.Significance Statement: Oral drug discovery programs typically strive for low clearance compounds to ensure sufficient target engagement. Human liver microsomes and isolated human hepatocytes are used to optimise and predict human hepatic metabolic clearance.