Organic anion transporting polypeptide (OATP) 1B1/3 mediated drug-drug interaction (DDI) potential is evaluated in vivo with rosuvastatin (RST) as a probe substrate in clinical studies. We calibrated our assay with RST and estradiol 17-β-D-glucuronide (E217βG)/ cholecystokinin-8 (CCK8) as in vitro probes for qualitative and quantitative prediction of OATP1B-mediated DDI potential for RST. In vitro OATP1B1/1B3 inhibition using E217βG and CCK8 yielded higher AUC ratio (AUCR) values numerically with the static model, but all probes performed similarly from a qualitative cutoff-based prediction, as described in regulatory guidances. However, the magnitudes of DDI were not captured satisfactorily. Considering that clearance of RST is also mediated by gut breast cancer resistance protein (BCRP), inhibition of BCRP was also incorporated in the DDI prediction if the gut inhibitor concentrations were 10 x IC50 for BCRP inhibition. This combined static model closely predicted the magnitude of RST DDI with root mean square error values of 0.767-0.812 and 1.24-1.31, with and without BCRP inhibition, respectively for in vitro-in vivo- correlation of DDI. Physiologically-based pharmacokinetic (PBPK) modeling was also used to simulate DDI between RST and rifampicin, asunaprevir, and velpatasvir. Predicted AUCR for rifampicin and asunaprevir was within 1.5-fold of that observed, whereas that for velpatasvir showed a 2-fold under-prediction. Overall, the combined static model incorporating both OATP1B and BCRP inhibition provides a quick and simple mathematical approach to quantitatively predict the magnitude of transporter-mediated DDI for RST for routine application. PBPK complemented the static model and provides a framework when a dynamic model is needed. Significance Statement Using 22 drugs, we show that a static model for OATP1B1/1B3 inhibition can qualitatively predict potential for DDI using a cut-off based approach as in regulatory guidances. However, consideration of both OATP1B1/3 and gut BCRP inhibition provided a better prediction of the magnitude of the transporter-mediated DDI of these inhibitors with rosuvastatin. Based on these results, we have proposed an empirical mechanistic-static approach for a more reliable prediction of transporter-mediated DDI liability with rosuvastatin that drug development teams can leverage.

Ipatasertib is a pan-AKT inhibitor in development for the treatment of cancer. Ipatasertib was metabolized by CYP3A4 to its major metabolite, M1 (G-037720), and was a P-gp substrate and OATP1B1/1B3 inhibitor in vitro. A Phase I drug-drug interaction (DDI) study (n=15) was conducted in healthy subjects to evaluate the effect of itraconazole (200 mg solution QD, 4 days), a strong CYP3A4 and P-gp inhibitor, on pharmacokinetics of ipatasertib (100 mg single dose). Itraconazole increased the C_max and AUC_0-Ꝏ of ipatasertib by 2.3- and 5.5- fold, respectively, increased the half-life by 53% and delayed the t_max by 1 hour. The C_max and AUC_0-72h of M1 (G-037720) reduced by 91% and 68%, respectively. This study confirmed that CYP3A4 plays a major role in ipatasertib clearance. Furthermore, the interaction of ipatasertib with coproporphyrin (CP)I and CPIII, the two endogenous substrates of OATP1B1/1B3, was evaluated in this study. CPI and CPIII plasma levels were unchanged in the presence of ipatasertib, both at exposures of 100 mg and at higher exposures in combination with itraconazole. This indicated no in vivo inhibition of OATP1B1/1B3 by ipatasertib. Additionally, it was shown that CPI and CPIII were not P-gp substrates in vitro and itraconazole had no effect on CPI and CPIII concentrations in vivo. The latter is an important finding because it will simplify interpretation of future DDI studies using CPI/CPIII as OATP1B1/1B3 biomarkers. Significance Statement In this drug-drug interaction study in healthy volunteers, we demonstrated that CYP3A4 plays a major role in ipatasertib clearance, and that ipatasertib is not an OATP1B1/1B3 inhibitor. Furthermore, it was demonstrated that itraconazole, an inhibitor of CYP3A4 and several transporters did not affect CPI/CPIII levels in vivo. This increases the understanding and application of these endogenous substrates as well as itraconazole in complex drug interaction studies.

Older adults remain significantly underrepresented in clinical trials. This is concerning because age‐associated physiological changes can alter drug pharmacokinetics and pharmacodynamics. This paper examines the status of older adult representation in clinical trials, analyzes contributing factors, and discusses initiatives to address gaps. Data were collected from studies completed between 2010 and 2025 using ClinicalTrials.gov to assess age‐related inclusion/exclusion criteria. Further, two surveys of pharmaceutical companies were initially conducted to assess age ranges of randomized patients in pivotal trials, what clinical pharmacology approaches were applied to potentially support dosing in older adult patients, whether exclusion criteria disproportionally excluded older patients, reasons older adult patients were not randomized, and whether older patients more often discontinued trials prematurely. Exclusion criterion based on an age cut‐off was rarely applied. Pharmacokinetic analyses were performed for most trials, but only one age‐related dosing recommendation was reported out of 29 products. In a survey of 41 clinical studies, exclusion criteria showed no bias toward exclusion of patients > 80 years. Reasons for not randomizing older patients were diverse and trial dependent. Subjective reasons for non‐randomization at higher frequencies in older patients were patient wish and doctor advice. Discontinuation rate did not reveal a clear age‐related trend. When comparing trial age composition to disease prevalence by age, a mismatch was evident with fewer than expected older patients enrolled. This research identifies the gap between trial participation and disease prevalence by age groups, offering insights for sponsor companies to enhance older adults' inclusion in clinical research.

Pralsetinib is a kinase inhibitor indicated for the treatment of metastatic rearranged during transfection (RET) fusion-positive non-small cell lung cancer. Pralsetinib is primarily eliminated by the liver and hence hepatic impairment (HI) is likely alter its pharmacokinetics (PK). Mild HI has been shown to have minimal impact on the PK of pralsetinib. This hepatic impairment study aimed to determine the pralsetinib PK, safety and tolerability in subjects with moderate and severe HI, as defined by the Child–Pugh and National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) classification systems, in comparison to subjects with normal hepatic function. Based on the Child–Pugh classification, subjects with moderate and severe HI had similar systemic exposure (area under the plasma concentration time curve from time 0 to infinity [AUC_0–∞]) to pralsetinib, with AUC_0–∞ geometric mean ratios (GMR) of 1.12 and 0.858, respectively, compared to subjects with normal hepatic function. Results based on the NCI-ODWG classification criteria were comparable; the AUC_0–∞ GMR were 1.22 and 0.858, respectively, for subjects with moderate and severe HI per NCI-ODWG versus those with normal hepatic function. These results suggested that moderate and severe hepatic impairment did not have a meaningful impact on the exposure to pralsetinib, thus not warranting a dose adjustment in this population.

Ipatasertib is a selective, small molecule Akt inhibitor that is currently being developed for the treatment of metastatic castration-resistant prostate cancer. Darolutamide is an androgen receptor (AR) inhibitor that is approved for the treatment of non-metastatic castration-resistant prostate cancer. Ipatasertib is metabolized by CYP3A4 to form a less active metabolite M1 (G-037720). Ipatasertib is also a weak time-dependent CYP3A4 inhibitor. Darolutamide is a mild CYP3A4 inducer and is metabolized into an active keto-darolutamide metabolite via CYP3A4. In this Phase 1b open-label, single sequence crossover study, ipatasertib pharmacokinetics safety and tolerability were evaluated in combination with darolutamide in metastatic castration-resistant prostate cancer (n = 15 patients). Specifically, the effect of 600 mg BID of darolutamide on 400 mg QD ipatasertib was evaluated in this study. Based on pharmacokinetic analysis, a mild reduction in ipatasertib AUC_{0–24 h,ss} and C_max,ss exposures was observed (~8% and ~21%, respectively) when administered in combination with darolutamide, which is considered not clinically meaningful. M1 exposures were similar with and without darolutamide administration. Darolutamide and keto-darolutamide exposures in combination with ipatasertib were similar to previously reported exposures for single agent darolutamide. Overall, the combination appears to be well-tolerated in the metastatic castration-resistant prostate cancer indication with very few AEs.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.