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Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f m,CYP3A4 , and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f m,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Physiologically based pharmacokinetic (PBPK) models of entrectinib and its equipotent metabolite, M5, were established in healthy adult subjects and extrapolated to pediatric patients to predict increases in steady-state systemic exposure on co-administration of strong and moderate CYP3A4 inhibitors (itraconazole at 5 mg/kg, erythromycin at 7.5-12.5 mg/kg and fluconazole at 3-12 mg/kg, respectively). Adult model establishment involved the optimization of fraction metabolized by CYP3A4 (0.92 for entrectinib and 0.98 for M5) using data from an itraconazole DDI study. This model captured well the exposure changes of entrectinib and M5 seen in adults co-administered with the strong CYP3A4 inducer rifampicin. In pediatrics, reasonable prediction of entrectinib and M5 pharmacokinetics in ≧2 year olds was achieved when using the default models for physiological development and enzyme ontogenies. However, a two to threefold misprediction of entrectinib and M5 exposures was seen in <2 year olds which may be due to missing mechanistic understanding of gut physiology and/or protein binding in very young children. Model predictions for ≧2 year olds showed that entrectinib AUC(0-t) was increased by approximately sevenfold and five to threefold by strong and high-moderate and low-moderate CYP3A4 inhibitors, respectively. Based on these victim DDI predictions, dose adjustments for entrectinib when given concomitantly with strong and moderate CYP3A4 inhibitors in pediatric subjects were recommended. These simulations informed the approved entrectinib label without the need for additional clinical pharmacology studies., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics © 2024 American Society for Clinical Pharmacology and Therapeutics.)

Typically, therapeutic proteins (TPs) have a low risk for eliciting meaningful drug interactions (DIs). However, there are select instances where TP drug interactions (TP-DIs) of clinical concern can occur. This white paper discusses the various types of TP-DIs involving mechanisms such as changes in disease state, target-mediated drug disposition, neonatal Fc receptor (FcRn), or antidrug antibodies formation. The nature of TP drug interaction being investigated should determine whether the examination is conducted as a standalone TP-DI study in healthy participants, in patients, or assessed via population pharmacokinetic analysis. DIs involving antibody-drug conjugates are discussed briefly, but the primary focus here will be DIs involving cytokine modulation. Cytokine modulation can occur directly by certain TPs, or indirectly due to moderate to severe inflammation, infection, or injury. Disease states that have been shown to result in indirect disease-DIs that are clinically meaningful have been listed (i.e., typically a twofold change in the systemic exposure of a coadministered sensitive cytochrome P450 substrate drug). Type of disease and severity of inflammation should be the primary drivers for risk assessment for disease-DIs. While more clinical inflammatory marker data needs to be collected, the use of two or more clinical inflammatory markers (such as C-reactive protein, albumin, or interleukin 6) may help broadly categorize whether the predicted magnitude of inflammatory disease-DI risk is negligible, weak, or moderate to strong. Based on current knowledge, clinical DI studies are not necessary for all TPs, and should no longer be conducted in certain disease patient populations such as psoriasis, which do not have sufficient systemic inflammation to cause a meaningful indirect disease-DI., (© 2022 The Authors. Clinical Pharmacology & Therapeutics © 2022 American Society for Clinical Pharmacology and Therapeutics.)

Failure to perform adequate dose adjustment in patients with liver cirrhosis may be associated with increased toxicity. We compared the prediction of area under the curve (AUC) and clearance for the six compounds of the Basel phenotyping cocktail (caffeine, efavirenz, flurbiprofen, omeprazole, metoprolol, and midazolam) using a well-known physiology-based pharmacokinetic approach (physiologically-based pharmacokinetic [PBPK] approach, Simcyp) and a novel top-down method based on the systemic clearance in healthy volunteers adjusted for markers of liver and renal dysfunction ("top-down approach"). With few exceptions, plasma concentration-time curves were accurately predicted by the PBPK approach. In comparison to the measured AUC and clearance of these drugs in patients with liver cirrhosis and healthy controls, except for efavirenz, the estimates of both approaches were within two standard deviations of the mean for total and free drug concentrations. For both approaches, a correction factor for dose adjustment in patients with liver cirrhosis could be calculated for the drugs administered. AUCs calculated using the adjusted doses were comparable to the AUCs measured in control subjects, with slightly more accurate predictions generated by the PBPK approach. For drugs with a free fraction < 50%, predictions using free drug concentrations were more accurate than with total drug concentrations. In conclusion, both methods provided good qualitative predictions of the changes by liver cirrhosis in the pharmacokinetics of the six compounds investigated. The top-down approach is easier to implement but the PBPK approach predicted changes in drug exposure more accurately than the top-down approach and provided reliable estimates for plasma concentrations., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Drug-drug interaction (DDI) assessments are well defined in health authority guidelines for small molecule drugs, and US Food and Drug Administration (FDA) draft guidance is now available for therapeutic proteins. However, there are currently no regulatory guidelines outlining DDI assessments for therapeutic peptides, which poses significant uncertainty and challenges during drug development for this heterogenous class of molecules. A cross-industry peptide DDI working group consisting of experts from 10 leading companies was formed under the sponsorship of the European Federation of Pharmaceutical Industries and Associations. We aimed to capture the range of DDI studies undertaken for peptide drugs by (i) anonymously surveying relevant companies involved in peptide drug development to better understand DDI study type/timing currently performed and (ii) compiling a database containing in vitro / clinical DDI data from submission packages for recently approved peptide drugs. Our analyses highlight significant gaps and uncertainty in the field. For example, the reported timing of in vitro peptide DDI studies, if performed, vary substantially across responding companies from early research to phase III. Nearly all in vitro cytochrome P450 / transporter inhibition studies reported in the survey were negative. For the few positive hits reported, no clinical follow-up studies were performed, questioning the clinical relevance of these findings. Furthermore, available submission packages reveal DDI likelihood is low for peptides >2 kDa, making it reasonable to adopt a risk-based approach during drug development for larger peptides. By benchmarking the landscape of peptide DDI activities across the industry, we set the stage for future discussions with health authorities on harmonizing peptide DDI approaches., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed., (© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.)

This study evaluated the health status of the mammary glands and milk composition of dairy goats. The California mastitis test (CMT) score, somatic cell counts (SCCs), somatic cell type, electrical conductivity (EC), N-acetyl-β-d-glucosaminidase (NAGase) activity, mastitis-causing pathogens, and milk composition in 121 udder-half milk samples from 62 crossbreed goats (1-3 years old) at 23-45 days postpartum were compared in four categories with SCCs of <200 × 10 3 , <300 × 10 3 , 301-1000 × 10 3 , and >1010 × 10 3  cells/ml. The SCC, CMT score, EC, and NAGase activity were significantly lower (p < 0.05) in udder-half milk with SCCs of <200 × 10 3 and <300 × 10 3  cells/ml than those in milk with SCCs of 301-1000 × 10 3 and >1010 × 10 3  cells/ml. The protein, lactose/ash, and solids-not-fat (SNF) in milk with an SCC of <300 × 10 3  cells/ml were similar to the previously reported values. The proportion of polymorphonuclear neutrophils was significantly higher (p < 0.05) in milk with SCCs of 301-1000 and >1010 × 10 3  cells/ml than in milk with an SCC of <300 × 10 3  cells/ml. Assessing mammary gland health and milk composition based on categorization of udder-half milk by SCC may be useful for milk quality control on goat farms., (© 2022 Japanese Society of Animal Science.)

Ribociclib is approved in combination with endocrine therapy as initial endocrine-based therapy for HR-positive and HER2-negative advanced breast cancer. Ribociclib is primarily metabolized by CYP3A4 and, in vitro, is an inhibitor of CYP3A and CYP1A2. Ritonavir (a strong CYP3A inhibitor) increased ribociclib 400 mg single-dose area under the plasma concentration-time curve (AUC) by 3.2-fold, whereas rifampin (a strong CYP3A inducer) decreased ribociclib AUC by 89% in healthy volunteers (HVs). Multiple 400 mg ribociclib doses increased midazolam (CYP3A substrate) AUC by 3.8-fold and caffeine (CYP1A2 substrate) AUC by 1.2-fold vs. each agent alone. A physiologically-based pharmacokinetic (PBPK) model was developed integrating in vitro, preclinical, and clinical data of HVs and patients with cancer. Data predictions indicated that multiple 600 mg ribociclib doses increased midazolam AUC by 5.85-fold and ritonavir increased ribociclib 600 mg multiple dose AUC by 1.31-fold in cancer patients. Based on pharmacokinetics, safety, and efficacy data, and PBPK modeling, dosing modifications for ribociclib recommend avoiding concurrent use of strong CYP3A inhibitors/inducers, and caution regarding using CYP3A substrates with narrow therapeutic indices., (© 2020 Novartis. Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

We predicted the drug-drug interaction (DDI) potential of siponimod in presence of cytochrome P450 (CYP)2C9/CYP3A4 inhibitors/inducers in subjects with different CYP2C9 genotypes by physiologically-based pharmacokinetic (PK) modeling. The model was established using in vitro and clinical PK data and verified by adequately predicting siponimod PK when coadministered with rifampin. With strong and moderate CYP3A4 inhibitors, an increased DDI risk for siponimod was predicted for CYP2C9*3/*3 genotype vs. other genotypes area under the curve ratio (AUCR): 3.03-4.20 vs. ≤ 1.49 for strong; 2.42 vs. 1.14-1.30 for moderate. AUCRs increased with moderate (2.13-2.49) and weak (1.12-1.42) CYP3A4/CYP2C9 inhibitors to the same extent for all genotypes. With strong CYP3A4/moderate CYP2C9 inducers and moderate CYP3A4 inducers, predicted AUCRs were 0.21-0.32 and 0.35-0.71, respectively. This complementary analysis to the clinical PK-DDI studies confirmed the relevant influence of CYP2C9 polymorphism on the DDI behavior of siponimod and represented the basis for the DDI labeling recommendations., (© 2019 Novartis Pharma AG. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Ribociclib (KISQALI), a cyclin-dependent kinase 4/6 inhibitor approved for the first-line treatment of HR+/HER2- advanced breast cancer with an aromatase inhibitor, is administered with no restrictions on concomitant gastric pH-elevating agents or food intake. The influence of proton pump inhibitors (PPIs) on ribociclib bioavailability was assessed using 1) biorelevant media solubility, 2) physiologically based pharmacokinetic (PBPK) modeling, 3) noncompartmental analysis (NCA) of clinical trial data, and 4) population PK (PopPK) analysis. This multipronged approach indicated no effect of gastric pH changes on ribociclib PK and served as a platform for supporting ribociclib labeling language, stating no impact of gastric pH-altering agents on the absorption of ribociclib, without a dedicated drug-drug interaction trial. The bioequivalence of ribociclib exposure with or without a high-fat meal was demonstrated in a clinical trial. Lack of restrictions on ribociclib dosing may facilitate better patient compliance and therefore clinical benefit., (© 2017 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Delamanid (OPC-67683, Deltyba™, nitro-dihydro-imidazooxazoles derivative) is approved for the treatment of adult pulmonary multidrug-resistant tuberculosis. The absorption, distribution and excretion of delamanid-derived radioactivity were investigated after a single oral administration of 14 C-delamanid at 3 mg/kg to rats. In both male and female rats, radioactivity in blood and all tissues reached peak levels by 8 or 24 h post-dose, and thereafter decreased slowly. Radioactivity levels were 3- to 5-fold higher in lung tissue at time to maximum concentration compared with plasma. In addition, radioactivity was broadly distributed in various tissues, including the central nervous system, eyeball, placenta and fetus, indicating that 14 C-delamanid permeated the brain, retinal and placental blood barriers. By 168 h post-dose, radioactivity in almost all the tissues was higher than that in the plasma. Radioactivity was also transferred into the milk of lactating rats. Approximately 6% and 92% of radioactivity was excreted in the urine and feces, respectively, indicating that the absorbed radioactivity was primarily excreted via the biliary route. No significant differences in the absorption, distribution and excretion of 14 C-delamanid were observed between male and female rats. The pharmacokinetic results suggested that delamanid was broadly distributed to the lungs and various tissues for a prolonged duration of time at concentrations expected to effectively target tuberculosis bacteria. These data indicate that delamanid, in addition to its previously demonstrated efficacy in pulmonary tuberculosis, might be an effective therapeutic approach to treating extrapulmonary tuberculosis. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Objectives: Membrane transporters and metabolism are major determinants of the hepatobiliary elimination of drugs. This work investigates several key questions for drug development. Such questions include which drugs demonstrate transporter-based clearance in the clinic, and which in vitro methods are most suitable for drug classification, i.e. transporter- vs metabolism-dependent compound class categories. Additional questions posed are: what is the expected quantitative change in exposure in the presence of a transporter- and/or metabolism-inhibiting drug, and which criteria should trigger follow-up clinical drug-drug interaction studies., Methods: A well-established method for (human) liver clearance prediction that considers all four physiological processes driving hepatic drug elimination (namely sinusoidal uptake and efflux, metabolism and biliary secretion) was applied. Suspended hepatocytes, liver microsomes and sandwich-cultured hepatocytes were used as in vitro models to determine the individual intrinsic clearance for 13 selected compounds with various physicochemical and pharmacokinetic properties., Results: Using this in vitro-in vivo extrapolation method a good linear correlation was observed between predicted and reported human hepatic clearances. Linear regression analysis revealed much improved correlations compared with other prediction methods., Conclusions: The presented approach serves as a basis for accurate compound categorization within the Biopharmaceutics Drug Disposition Classification System (BDDCS) and was applied to anticipate metabolism- and transporter-based drug-drug interactions using different static prediction methods. A decision tree proposal is provided and helps to guide clinical studies on active processes influencing hepatic elimination. All recommendations in this paper are generally intended to support early pre-clinical and clinical drug development and the filing of a new drug application., (Copyright © 2012 John Wiley & Sons, Ltd.)