Survey of Pharmaceutical Industry's Best Practices around In Vitro Transporter Assessment and Implications for Drug Development: Considerations from the International Consortium for Innovation and Quality for Pharmaceutical Development Transporter Working Group.

The International Consortium for Innovation and Quality in Pharmaceutical Development Transporter Working Group had a rare opportunity to analyze a crosspharma collation of in vitro data and assay methods for the evaluation of drug transporter substrate and inhibitor potential. Experiments were generally performed in accordance with regulatory guidelines. Discrepancies, such as not considering the impact of preincubation for inhibition and free or measured in vitro drug concentrations, may be due to the retrospective nature of the dataset and analysis. Lipophilicity was a frequent indicator of crosstransport inhibition (P-gp, BCRP, OATP1B, and OCT1), with high molecular weight (MW ≥500 Da) also common for OATP1B and BCRP inhibitors. A high level of overlap in in vitro inhibition across transporters was identified for BCRP, OATP1B1, and MATE1, suggesting that prediction of DDIs for these transporters will be common. In contrast, inhibition of OAT1 did not coincide with inhibition of any other transporter. Neutrals, bases, and compounds with intermediate-high lipophilicity tended to be P-gp and/or BCRP substrates, whereas compounds with MW <500 Da tended to be OAT3 substrates. Interestingly, the majority of in vitro inhibitors were not reported to be followed up with a clinical study by the submitting company, whereas those compounds identified as substrates generally were. Approaches to metabolite testing were generally found to be similar to parent testing, with metabolites generally being equally or less potent than parent compounds. However, examples where metabolites inhibited transporters in vitro were identified, supporting the regulatory requirement for in vitro testing of metabolites to enable integrated clinical DDI risk assessment. SIGNIFICANCE STATEMENT: A diverse dataset showed that transporter inhibition often correlated with lipophilicity and molecular weight (>500 Da). Overlapping transporter inhibition was identified, particularly that inhibition of BCRP, OATP1B1, and MATE1 was frequent if the compound inhibited other transporters. In contrast, inhibition of OAT1 did not correlate with the other drug transporters tested.

The Absolute Bioavailability and Absorption, Metabolism, and Excretion of Ipatasertib, a Potent and Highly Selective Protein Kinase B (Akt) Inhibitor.

Ipatasertib (GDC-0068) is a potent, highly selective, small-molecule inhibitor of protein kinase B (Akt) being developed by Genentech/Roche as a single agent and in combination with other therapies for the treatment of cancers. To fully understand the absorption, metabolism, and excretion of ipatasertib in humans, an open-label study using 14C-radiolabeled ipatasertib was completed to characterize the absolute bioavailability (period 1) and mass balance and metabolite profiling (period 2). In period 1, subjects were administered a 200 mg oral dose of ipatasertib followed by an 80 µg (800 nCi) intravenous dose of [14C]-ipatasertib. In period 2, subjects received a single oral dose containing approximately 200 mg (100 µCi) [14C]-ipatasertib. In an integrated analytical strategy, accelerator mass spectrometry was applied to measure the 14C microtracer intravenous pharmacokinetics in period 1 and fully profile plasma radioactivity in period 2. The systemic plasma clearance and steady-state volume of distribution were 98.8 L/h and 2530 L, respectively. The terminal half-lives after oral and intravenous administrations were similar (26.7 and 27.4 hours, respectively) and absolute bioavailability of ipatasertib was 34.0%. After a single oral dose of [14C]-ipatasertib, 88.3% of the administered radioactivity was recovered with approximately 69.0% and 19.3% in feces and urine, respectively. Radioactivity in feces and urine was predominantly metabolites with 24.4% and 8.26% of dose as unchanged parent, respectively; indicating that ipatasertib had been extensively absorbed and hepatic metabolism was the major route of clearance. The major metabolic pathway was N-dealkylation mediated by CYP3A, and minor pathways were oxidative by cytochromes P450 and aldehyde oxidase. SIGNIFICANCE STATEMENT: The study provided definitive information regarding the absolute bioavailability and the absorption, metabolism, and excretion pathways of ipatasertib, a potent, novel, and highly selective small-molecule inhibitor of protein kinase B (Akt). An ultrasensitive radioactive counting method, accelerator mass spectrometry was successfully applied for 14C-microtracer absolute bioavailability determination and plasma metabolite profiling.

Advancing drug development in pediatric oncology, a focus on cancer biology and targeted therapies: iMATRIX platform.

With the development of novel treatment therapies as well as evolving and innovative approaches to conduct clinical trials, the landscape of pediatric oncology drug development has dramatically changed in recent years. Despite this change, approvals for new drugs and labeling updates to ensure availability of proper treatment for pediatric patients with cancer remain slow. The context of drug development in pediatric tumors has also changed with regulatory initiatives in the US and Europe, creating a great need for faster development of novel drugs. Today, conventional study designs have been replaced or complemented by novel clinical trial designs, such as master protocols and platform trials, to optimize cancer drug development and enable faster regulatory approval. The iMATRIX platform is a mechanism-of-action (MOA)-based phase 1/2 trial framework for concurrently studying multiple molecules across a range of relevant pediatric tumor types, taking into account the biology of each pediatric tumor type. Six studies have been conducted, ongoing, or planned on the iMATRIX platform - investigating atezolizumab, cobimetinib, entrectinib, idasanutlin, alectinib, and glofitamab. A brief overview of study designs and characteristics are shared in this article, along with learnings from them.

The effect of BI 409306 on heart rate in healthy volunteers: a randomised, double-blind, placebo-controlled, crossover study.

PURPOSE: The potent, selective phosphodiesterase-9A inhibitor BI 409306 may be beneficial for patients with attenuated psychosis syndrome and could prevent relapse in patients with schizophrenia. Transient BI 409306-dependent increases in heart rate (HR) demonstrated previously necessitated cardiac safety characterisation. We evaluated cardiac effects of BI 409306 in healthy volunteers during rest and exercise. METHODS: In this double-blind, three-way crossover study, volunteers received placebo, BI 409306 50 mg or 200 mg in randomised order (same treatment on Days 1 [resting] and 3 [exercise]). Cardiopulmonary exercise testing was performed twice post treatment on Day 3 of each period. BI 409306-mediated effects on placebo-corrected change from baseline in resting HR (ΔΔHR) were evaluated based on exposure-response analysis and a random coefficient model. Adverse events (AEs) were recorded. RESULTS: Overall, 19/20 volunteers completed. Resting ΔΔHR versus BI 409306 concentration yielded a slope of 0.0029 beats/min/nmol/L. At the geometric mean (gMean) maximum plasma concentration (Cmax) for BI 409306 50 and 200 mg, predicted mean (90% CI) ΔΔHRs were 0.80 (- 0.76, 2.36) and 5.46 (2.44, 8.49) beats/min, respectively. Maximum adjusted mean differences from placebo (90% CI) in resting HR for BI 409306 50 and 200 mg were 3.85 (0.73, 6.97) and 4.93 (1.69, 8.16) beats/min. Maximum differences from placebo in resting HR occurred at/near gMean Cmax and returned to baseline after approximately 4 h. The proportion of volunteers with AEs increased with BI 409306 dose. CONCLUSION: Observed hemodynamic effects following BI 409306 administration were of low amplitude, transient, and followed the pharmacokinetic profile of BI 409306.

Evaluation of Ipatasertib Interactions with Itraconazole and Coproporphyrin I and III in a Single Drug Interaction Study in Healthy Subjects.

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 Cmax and AUC0 -∞ of ipatasertib by 2.3- and 5.5-fold, respectively, increased the half-life by 53%, and delayed the tmax by 1 hour. The Cmax and AUC0-72h of its metabolite 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: This drug-drug interaction study in healthy volunteers demonstrated that CYP3A4 plays a major role in ipatasertib clearance, and that ipatasertib is not an organic anion transporting polypeptide 1B1/1B3 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.

Calibrating the In Vitro-In Vivo Correlation for OATP-Mediated Drug-Drug Interactions with Rosuvastatin Using Static and PBPK Models.

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-ß-D-glucuronide (E217ßG)/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 E217ßG and CCK8 yielded higher area under the curve (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 × 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 underprediction. 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 complements the static model and provides a framework for studying molecules when a dynamic model is needed. SIGNIFICANCE STATEMENT: Using 22 drugs, we show that a static model for organic anion-transporting polypeptide (OATP) 1B1/1B3 inhibition can qualitatively predict potential for drug-drug interaction (DDI) using a cutoff-based approach, as in regulatory guidances. However, consideration of both OATP1B1/3 and gut breast cancer resistance protein 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.

Contribution of Major Metabolites toward Complex Drug-Drug Interactions of Deleobuvir: In Vitro Predictions and In Vivo Outcomes.

The drug-drug interaction (DDI) potential of deleobuvir, an hepatitis C virus (HCV) polymerase inhibitor, and its two major metabolites, CD 6168 (formed via reduction by gut bacteria) and deleobuvir-acyl glucuronide (AG), was assessed in vitro. Area-under-the-curve (AUC) ratios (AUCi/AUC) were predicted using a static model and compared with actual AUC ratios for probe substrates in a P450 cocktail of caffeine (CYP1A2), tolbutamide (CYP2C9), and midazolam (CYP3A4), administered before and after 8 days of deleobuvir administration to HCV-infected patients. In vitro studies assessed inhibition, inactivation and induction of P450s. Induction was assessed in a short-incubation (10 hours) hepatocyte assay, validated using positive controls, to circumvent cytotoxicity seen with deleobuvir and its metabolites. Overall, P450 isoforms were differentially affected by deleobuvir and its two metabolites. Of note was more potent CYP2C8 inactivation by deleobuvir-AG than deleobuvir and P450 induction by CD 6168 but not by deleobuvir. The predicted net AUC ratios for probe substrates were 2.92 (CYP1A2), 0.45 (CYP2C9), and 0.97 (CYP3A4) compared with clinically observed ratios of 1.64 (CYP1A2), 0.86 (CYP2C9), and 1.23 (CYP3A4). Predictions of DDI using deleobuvir alone would have significantly over-predicted the DDI potential for CYP3A4 inhibition (AUC ratio of 6.15). Including metabolite data brought the predicted net effect close to the observed DDI. However, the static model over-predicted the induction of CYP2C9 and inhibition/inactivation of CYP1A2. This multiple-perpetrator DDI scenario highlights the application of the static model for predicting complex DDI for CYP3A4 and exemplifies the importance of including key metabolites in an overall DDI assessment.

Mass balance, metabolite profile, and in vitro-in vivo comparison of clearance pathways of deleobuvir, a hepatitis C virus polymerase inhibitor.

The pharmacokinetics, mass balance, and metabolism of deleobuvir, a hepatitis C virus (HCV) polymerase inhibitor, were assessed in healthy subjects following a single oral dose of 800 mg of [(14)C]deleobuvir (100 µCi). The overall recovery of radioactivity was 95.2%, with 95.1% recovered from feces. Deleobuvir had moderate to high clearance, and the half-life of deleobuvir and radioactivity in plasma were ∼ 3 h, indicating that there were no metabolites with half-lives significantly longer than that of the parent. The most frequently reported adverse events (in 6 of 12 subjects) were gastrointestinal disorders. Two major metabolites of deleobuvir were identified in plasma: an acyl glucuronide and an alkene reduction metabolite formed in the gastrointestinal (GI) tract by gut bacteria (CD 6168), representing ∼ 20% and 15% of the total drug-related material, respectively. Deleobuvir and CD 6168 were the main components in the fecal samples, each representing ∼ 30 to 35% of the dose. The majority of the remaining radioactivity found in the fecal samples (∼ 21% of the dose) was accounted for by three metabolites in which deleobuvir underwent both alkene reduction and monohydroxylation. In fresh human hepatocytes that form biliary canaliculi in sandwich cultures, the biliary excretion for these excretory metabolites was markedly higher than that for deleobuvir and CD 6168, implying that rapid biliary elimination upon hepatic formation may underlie the absence of these metabolites in circulation. The low in vitro clearance was not predictive of the observed in vivo clearance, likely because major deleobuvir biotransformation occurred by non-CYP450-mediated enzymes that are not well represented in hepatocyte-based in vitro models.

Defining the Role of Gut Bacteria in the Metabolism of Deleobuvir: In Vitro and In Vivo Studies.

Deleobuvir is a potent inhibitor of the hepatitis C virus nonstructural protein 5B polymerase. In humans, deleobuvir underwent extensive reduction to form CD 6168. This metabolite was not formed in vitro in aerobic incubations with human liver microsomes or cytosol. Anaerobic incubations of deleobuvir with rat and human fecal homogenates produced CD 6168. Using these in vitro formation rates, a retrospective analysis was conducted to assess whether the fecal formation of CD 6168 could account for the in vivo levels of this metabolite. The formation of CD 6168 was also investigated using a pseudo-germ free (pGF) rat model, in which gut microbiota were largely eradicated by antibiotic treatment. Plasma exposure (area under the curve from 0 to ∞) of CD 6168 was approximately 9-fold lower in pGF rats (146 ± 64 ng·h/ml) compared with control rats (1,312 ± 649 ng·h/ml). Similarly, in pGF rats, lower levels of CD 6168 (1.5% of the deleobuvir dose) were excreted in feces compared with control rats (42% of the deleobuvir dose). In agreement with these findings, in pGF rats, approximately all of the deleobuvir dose was excreted as deleobuvir into feces (105% of dose), whereas only 26% of the deleobuvir dose was excreted as deleobuvir in control rats. These differences in plasma and excretion profiles between pGF and control rats confirm the role of gut bacteria in the formation of CD 6168. These results underline the importance of evaluating metabolism by gut bacteria and highlight experimental approaches for nonclinical assessment of bacterial metabolism in drug development.

Mechanisms underlying benign and reversible unconjugated hyperbilirubinemia observed with faldaprevir administration in hepatitis C virus patients.

Faldaprevir, an investigational agent for hepatitis C virus treatment, is well tolerated but associated with rapidly reversible, dose-dependent, clinically benign, unconjugated hyperbilirubinemia. Multidisciplinary preclinical and clinical studies were used to characterize mechanisms underlying this hyperbilirubinemia. In vitro, faldaprevir inhibited key processes involved in bilirubin clearance: UDP glucuronosyltransferase (UGT) 1A1 (UGT1A1) (IC50 0.45 µM), which conjugates bilirubin, and hepatic uptake and efflux transporters, organic anion-transporting polypeptide (OATP) 1B1 (IC50 0.57 µM), OATP1B3 (IC50 0.18 µM), and multidrug resistance-associated protein (MRP) 2 (IC50 6.2 µM), which transport bilirubin and its conjugates. In rat and human hepatocytes, uptake and biliary excretion of [(3)H]bilirubin and/or its glucuronides decreased on coincubation with faldaprevir. In monkeys, faldaprevir (≥20 mg/kg per day) caused reversible unconjugated hyperbilirubinemia, without hemolysis or hepatotoxicity. In clinical studies, faldaprevir-mediated hyperbilirubinemia was predominantly unconjugated, and levels of unconjugated bilirubin correlated with the UGT1A1*28 genotype. The reversible and dose-dependent nature of the clinical hyperbilirubinemia was consistent with competitive inhibition of bilirubin clearance by faldaprevir, and was not associated with liver toxicity or other adverse events. Overall, the reversible, unconjugated hyperbilirubinemia associated with faldaprevir may predominantly result from inhibition of bilirubin conjugation by UGT1A1, with inhibition of hepatic uptake of bilirubin also potentially playing a role. Since OATP1B1/1B3 are known to be involved in hepatic uptake of circulating bilirubin glucuronides, inhibition of OATP1B1/1B3 and MRP2 may underlie isolated increases in conjugated bilirubin. As such, faldaprevir-mediated hyperbilirubinemia is not associated with any liver injury or toxicity, and is considered to result from decreased bilirubin elimination due to a drug-bilirubin interaction.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors.

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

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.

Exploring the Impact of Hepatic Impairment on Pralsetinib Pharmacokinetics.

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 [AUC0-∞]) to pralsetinib, with AUC0-∞ 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 AUC0-∞ 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.

Evaluation of Patient-Centric Sample Collection Technologies for Pharmacokinetic Assessment of Large and Small Molecules.

Low-volume sampling devices offer the promise of lower discomfort and greater convenience for patients, potentially reducing patient burden and enabling decentralized clinical trials. In this study, we determined whether low-volume sampling devices produce pharmacokinetic (PK) data comparable to conventional venipuncture for a diverse set of monoclonal antibodies (mAbs) and small molecules. We adopted an open-label, non-randomized, parallel-group, single-site study design, with four cohorts of 10 healthy subjects per arm. The study drugs, doses, and routes of administration included: crenezumab (15 mg/kg, intravenous infusion), etrolizumab (210 mg, subcutaneous), GDC-X (oral), and hydroxychloroquine (HCQ, 200 mg, oral). Samples were collected after administration of a single dose of each drug using conventional venipuncture and three low-volume capillary devices: TassoOne Plus for liquid blood, Tasso-M20 for dry blood, both applied to the arm, and Neoteryx Mitra® for dry blood obtained from fingertips. Serum/plasma concentrations from venipuncture and TassoOne Plus samples overlapped and PK parameters were comparable for all drugs, except HCQ. After applying a baseline hematocrit value, the dry blood concentrations and PK parameters for the two monoclonal antibodies were comparable to those obtained from venipuncture. For the two small molecules, two bridging strategies were evaluated for converting dry blood concentrations to equivalent plasma concentrations. A baseline hematocrit correction and/or linear regression-based correction was effective for GDC-X, but not for HCQ. Additionally, the study evaluated the bioanalytical data quality and comparability from the various collection methods, as well as patient preference for the devices.

Impact of P-gp inhibition on systemic exposure of pralsetinib and dosing considerations.

A study to determine the impact of cyclosporine (Neoral), an inhibitor of P-gp, on the pharmacokinetics of pralsetinib (trade name GAVRETO®) was conducted in 15 healthy adult volunteers. A single 200 mg dose of pralsetinib was administered orally alone and in combination with cyclosporine with a 9-day washout between treatments. Co-administration with cyclosporine resulted in a clinically relevant increase in pralsetinib maximum plasma concentration (Cmax) and area under the plasma concentration-time curve extrapolated to infinity (AUC0-∞) with associated geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of 148% (109, 201) and 181% (136, 241), respectively. These findings provide insight into concomitant dosing of pralsetinib with inhibitors of P-gp given the increases in pralsetinib exposure observed when administered with cyclosporine. Based on these results, co-administration of pralsetinib with P-gp inhibitors is not recommended. In the event that co-administration cannot be avoided, it is recommended that the dose of pralsetinib be reduced.

Pharmacokinetics (PK) of Tiragolumab in First-in-Human Study in Patients with Mixed Solid Tumors (GO30103).

Tiragolumab is a first-in-class, fully human IgG1/kappa anti-TIGIT monoclonal antibody that blocks the binding of TIGIT to CD155 (the poliovirus receptor). We summarize the pharmacokinetics (PK) data from the phase 1a/1b GO30103 study of Q3W (every 3 weeks) sequential dosing of tiragolumab (2, 8, 30, 100, 400, 600, or 1200 mg) followed by atezolizumab (1200 mg), Q4W (every 4 weeks) sequential dosing (tiragolumab 840 mg followed by atezolizumab 1680 mg), and Q4W co-infusion (tiragolumab 840 mg plus atezolizumab 1680 mg). Serum samples were collected at multiple time points following tiragolumab and atezolizumab intravenous infusion in patients with solid tumors for PK and immunogenicity assessment. The serum PK profile of tiragolumab appeared to be biphasic, with a rapid distribution phase followed by a slower elimination phase when administered alone or in combination with atezolizumab. In phase 1a, across doses of tiragolumab ranging from 2 to 1200 mg (cycle 1), the geometric mean (GM), coefficient of variation (CV%), serum tiragolumab Cmax ranged from 0.682 to 270 µg/mL (18.6% to 36.5%) and Cmin ranged from 0.0125 to 75.3 µg/mL (0.0% to 24.2%). The GM systemic exposure (area under the plasma drug concentration-time curve, AUC0-21) ranged from 310 to 2670 µg day/mL (20.5% to 27.0%); interindividual variability in AUC0-21 ranged from 20.5% to 43.9%. Tiragolumab exposure increased in an approximately dose-proportional manner when administered alone or with atezolizumab at doses ≥100 mg. Postbaseline, 4/207 patients (1.9%) were positive for treatment-emergent antidrug antibodies (ADA) against tiragolumab, each at a single time point. Tiragolumab combined with atezolizumab demonstrated desirable PK properties, with no drug-drug interactions or immunogenicity liability. There were no meaningful differences in tiragolumab or atezolizumab exposure between the Q4W co-infusion and sequential dosing cohorts. ClinicalTrials.gov: NCT02794571 (date of registration June 6, 2016).

Risk Factors of Hyperglycemia After Treatment With the AKT Inhibitor Ipatasertib in the Prostate Cancer Setting: A Machine Learning-Based Investigation.

PURPOSE: Hyperglycemia is a major adverse event of phosphatidylinositol 3-kinase/AKT inhibitor class of cancer therapeutics. Machine learning (ML) methodologies can identify and highlight how explanatory variables affect hyperglycemia risk. METHODS: Using data from clinical trials of the AKT inhibitor ipatasertib (IPAT) in the metastatic castrate-resistant prostate cancer setting, we trained an XGBoost ML model to predict the incidence of grade ≥2 hyperglycemia (HGLY ≥ 2). Of the 1,364 patients included in our analysis, 19.4% (n = 265) of patients had HGLY ≥2 events with a median time of first onset of 28 days (range, 0-753 days), and 30.0% (n = 221) of patients on an IPAT regimen had at least one HGLY ≥2 event compared with 7.0% (n = 44) of patients on placebo. RESULTS: An 11-variable XGBoost model predicted HGLY ≥2 events well with an AUROC of 0.83 ± 0.02 (mean ± standard deviation). Using SHapley Additive exPlanations analysis, we found IPAT exposure and baseline HbA1c levels to be the strongest predictors of HGLY ≥2, with additional predictivity of baseline measurements of fasting glucose, magnesium, and high-density lipoproteins. CONCLUSION: The findings support using patients' prediabetic status as a key factor for hyperglycemia monitoring and/or trial exclusion criteria. Additionally, the model and relationships between explanatory variables and HGLY ≥2 described herein can help identify patients at high risk for hyperglycemia and develop rational risk mitigation strategies.

Safety Profile of Ipatasertib Plus Abiraterone vs Placebo Plus Abiraterone in Metastatic Castration-resistant Prostate Cancer.

PURPOSE: Adding ipatasertib to abiraterone and prednisone/prednisolone significantly improved radiographic progression-free survival for patients with metastatic castration-resistant prostate cancer (mCRPC) with PTEN-loss tumours by immunohistochemistry in the IPATential150 trial (NCT03072238). Here we characterise the safety of these agents in subpopulations and assess manageability of key adverse events (AEs). MATERIALS AND METHODS: In this randomised, double-blind, phase 3 trial, patients with previously untreated asymptomatic or mildly symptomatic mCRPC were randomised 1:1 to receive ipatasertib-abiraterone or placebo-abiraterone (all with prednisone/prednisolone). AEs were analysed, focusing on key AEs of diarrhoea, hyperglycaemia, rash and transaminase increased. RESULTS: 1097 patients received study medication and were assessed for safety (47% with PTEN-loss tumours by immunohistochemistry and 20% were Asian). Ipatasertib was associated with increased Grade 3/4 AEs and AEs leading to treatment discontinuation vs placebo. The rate of discontinuation of ipatasertib was 18% in patients with PTEN-loss and 21% overall. The frequencies of all-grade, Grade 3/4 and serious AEs were similar between the PTEN-loss and overall populations. Diarrhoea, hyperglycaemia, rash and transaminase elevation were more frequent in ipatasertib-treated patients, appearing rapidly after treatment initiation (median onset: 8-43 days for ipatasertib arm and 56-104 days for placebo). The ipatasertib discontinuation rate was 32% and 18% in Asian and non-Asian patients, respectively, despite similar baseline characteristics and Grade 3/4 AE frequencies between groups. CONCLUSIONS: Ipatasertib plus abiraterone had an overall tolerable safety profile consistent with known toxicities. More AEs leading to drug discontinuation were observed with ipatasertib than placebo, but incidence would likely be lessened with prophylactic measures.

Drug-Drug Interaction Study to Evaluate the Pharmacokinetics, Safety, and Tolerability of Ipatasertib in Combination with Darolutamide in Patients with Advanced Prostate Cancer.

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 AUC0-24 h,ss and Cmax,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.

Assessment of cytochrome P450 3A4-mediated drug-drug interactions for ipatasertib using a fit-for-purpose physiologically based pharmacokinetic model.

PURPOSE: Ipatasertib, a potent and highly selective small-molecule inhibitor of AKT, is currently under investigation for treatment of cancer. Ipatasertib is a substrate and a time-dependent inhibitor of CYP3A4. It exhibits non-linear pharmacokinetics at subclinical doses in the clinical dose escalation study. To assess the DDI risk of ipatasertib at the intended clinical dose of 400 mg with CYP3A4 inhibitors, inducers, and substrates, a fit-for-purpose physiologically based pharmacokinetic (PBPK) model of ipatasertib was developed. METHODS: The PBPK model was constructed in Simcyp using in silico, in vitro, and clinical data and was optimized and verified using clinical data. RESULTS: The PBPK model described non-linear pharmacokinetics of ipatasertib and captured the magnitude of the observed clinical DDIs. Following repeated doses of 400 mg ipatasertib once daily (QD), the PBPK model predicted a 3.3-fold increase of ipatasertib exposure with itraconazole; a 2-2.5-fold increase with moderate CYP3A4 inhibitors, erythromycin and diltiazem; and no change with a weak CYP3A4 inhibitor, fluvoxamine. Additionally, in the presence of strong or moderate CYP3A4 inducers, rifampicin and efavirenz, ipatasertib exposures were predicted to decrease by 86% and 74%, respectively. As a perpetrator, the model predicted that ipatasertib (400 mg) caused a 1.7-fold increase in midazolam exposure. CONCLUSION: This study demonstrates the value of using a fit-for-purpose PBPK model to assess the clinical DDIs for ipatasertib and to provide dosing strategies for the concurrent use of other CYP3A4 perpetrators or victims.