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Purpose Entrectinib is a central nervous system-active potent inhibitor of tropomyosin receptor kinase (TRK), with anti-tumor activity against neurotrophic NTRK gene fusion-positive tumors. This study investigates the pharmacokinetics of entrectinib and its active metabolite (M5) in pediatric patients and aims to understand whether the pediatric dose of 300 mg/m2 once daily (QD) provides an exposure that is consistent with the approved adult dose (600 mg QD). Methods Forty-three patients aged from birth to 22 years were administered entrectinib (250–750 mg/m2 QD) orally with food in 4-week cycles. Entrectinib formulations included capsules without acidulant (F1) and capsules with acidulant (F2B and F06). Results Although there was interpatient variability with F1, entrectinib and M5 exposures increased dose dependently. Lower systemic exposures were observed in pediatric patients receiving 400 mg/m2 QD entrectinib (F1) versus adults receiving either the same dose/formulation or the recommended flat dose of 600 mg QD (~ 300 mg/m2 for a 70 kg adult) due to suboptimal F1 performance in the pediatric study. The observed pediatric exposures following 300 mg/m2 QD entrectinib (F06) were comparable to those in adults receiving 600 mg QD. Conclusions Overall, the F1 formulation of entrectinib was associated with lower systemic exposure in pediatric patients compared with the commercial acidulant formulation (F06). Systemic exposures achieved in pediatric patients with the F06 recommended dose (300 mg/m2) were within the known efficacious range in adults, confirming the adequacy of the recommended dose regimen with the commercial formulation.

Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro–in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug–drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

SummaryBackground: Entrectinib is an oral, CNS-active, potent inhibitor of tyrosine receptor kinases A/B/C, tyrosine kinase ROS proto-oncogene 1, and anaplastic lymphoma kinase approved for use in patients with solid tumors. We describe 3 clinical studies, including one investigating the single/multiple dose pharmacokinetics of entrectinib in patients and two studies in healthy volunteers investigating the absorption/distribution/metabolism/excretion (ADME) of entrectinib, its relative bioavailability, and effect of food on pharmacokinetics. Methods: The patient study is open-label with dose-escalation and expansion phases. Volunteers received entrectinib (100–400 mg/m2, and 600–800 mg) once daily with food in continuous 28-day cycles. In the ADME study, volunteers received a single oral dose of [14C]entrectinib 600 mg. In the third study, volunteers received single doses of entrectinib 600 mg as the research and marketed formulations in the fasted state (Part 1), and the marketed formulation in the fed and fasted states (Part 2). Entrectinib and its major active metabolite M5 were assessed in all studies. Results: Entrectinib was absorbed in a dose-dependent manner with maximum concentrations at ~4 h postdose and an elimination half-life of ~20 h. Entrectinib was cleared mainly through metabolism and both entrectinib and metabolites were eliminated mainly in feces (minimal renal excretion). At steady-state, the M5-to-entrectinib AUC ratio was 0.5 (with 600 mg entrectinib research formulation in patients). The research and marketed formulations were bioequivalent and food had no relevant effect on pharmacokinetics. Conclusions: Entrectinib is well absorbed, with linear PK that is suitable for once-daily dosing, and can be taken with or without food.

Waterfall plots are used to describe changes in tumor size observed in clinical studies. They are frequently used to illustrate the overall drug response in oncology clinical trials because of its simple representation of results. Unfortunately, this visual display suffers a number of limitations including (1) potential misguidance by masking the time dynamics of tumor size, (2) ambiguous labelling of the y-axis, and (3) low data-to-ink ratio. We offer some alternatives to address these shortcomings and recommend moving away from waterfall plots to the benefit of plots showing the individual time profiles of sum of lesion diameters (according to RECIST). The spider plot presents the individual changes in tumor measurements over time relative to baseline tumor burden. Baseline tumor size is a well-known confounding factor of drug effect which has to be accounted for when analyzing data in early clinical trials. While spider plots are conveniently correct for baseline tumor size, they cannot be presented in isolation. Indeed, percentage change from baseline has suboptimal statistical properties (including skewed distribution) and can be overly optimistic in favor of drug efficacy. We argued that plots of raw data (referred to as spaghetti plots) should always accompany spider plots to provide an equipoised illustration of the drug effect on lesion diameters.

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony-stimulating factor-1 receptor. Here, we show how modeling target-mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three-fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse-type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

The anaplastic lymphoma kinase (ALK) inhibitor alectinib is an effective treatment for ALK-positive non-small-cell lung cancer. This bioequivalence study evaluated the in vivo performance of test 3 formulations with the reduced wetting agent sodium lauryl sulfate (SLS) content. This randomized, 4-period, 4-sequence, crossover study compared alectinib (600 mg) as 25%, 12.5%, and 3% SLS hard capsule formulations with the reference 50% SLS clinical formulation in healthy subjects under fasted conditions (n = 49), and following a high-fat meal (n = 48). Geometric mean ratios and 90% confidence intervals (CIs) for Cmax , AUC0-last , and AUC0-∞ of alectinib, its major active metabolite, M4, and alectinib plus M4 were determined for the test formulations versus the reference formulation. Bioequivalence was concluded if the 90%CIs were within the 80% to 125% boundaries. The 25% SLS formulation demonstrated bioequivalence to the reference 50% SLS formulation for Cmax , AUC0-last , and AUC0-∞ of alectinib, M4, and alectinib plus M4 under both fasted and fed conditions. Further reductions in SLS content (12.5% and 3% SLS) did not meet the bioequivalence criteria. Cross-group comparisons showed an approximately 3-fold positive food effect. Reducing SLS to 25% resulted in a formulation that is bioequivalent to the current 50% SLS formulation used in alectinib pivotal trials.

Alectinib, an anaplastic lymphoma kinase (ALK) inhibitor, is approved for treatment of patients with ALK+ non-small cell lung cancer who have progressed, on or are intolerant to, crizotinib. This study assessed the effect of a high-fat meal and the proton pump inhibitor, esomeprazole, on the pharmacokinetics (PK) of alectinib. This was an open-label, 2-group study in healthy subjects. In group 1 (n = 18), subjects were randomly assigned to a 2-treatment (A, fasted conditions; B, following a high-fat meal), 2-sequence (AB or BA) crossover assessment, separated by a 10-day washout. In group 2 (n = 24), subjects were enrolled in a 2-period, fixed-sequence crossover assessment to evaluate the effect of esomeprazole. PK parameters were evaluated for alectinib, its major similarly active metabolite, M4, and the combined exposure of alectinib and M4. Administration of alectinib following a high-fat meal substantially increased the combined exposure of alectinib and M4 to 331% (90%CI, 279%-393%) and 311% (90%CI, 273%-355%) for Cmax and AUC0-∞ , respectively, versus fasted conditions. Coadministration of esomeprazole had no clinically relevant effect on the combined exposure of alectinib and M4. Alectinib should be administered under fed conditions to maximize its bioavailability, whereas no restrictions are required with antisecretory agents.

The emergence of genetic data linking Nav1.7 sodium channel over- and under- expression to human pain signalling has led to an interest in the treatment of chronic pain through inhibition of Nav1.7 channels. We describe the pharmacokinetic (PK) results of a clinical microdose study performed with four potent and selective Nav1.7 inhibitors and the subsequent modelling resulting in the selection of a single compound to explore Nav1.7 pharmacology at higher doses. A clinical microdose study to investigate the intravenous and oral PK of four compounds (PF-05089771, PF-05150122, PF-05186462 and PF-05241328) was performed in healthy volunteers. PK parameters were derived via noncompartmental analysis. A physiologically-based PK (PBPK) model was used to predict exposure and multiples of Nav1.7 50 % inhibitory concentration (IC50) for each compound at higher doses. Plasma clearance, volume of distribution and bioavailability ranged from 45 to 392 mL/min/kg, 13 to 36 L/kg and 38 to 110 %, respectively. The PBPK model for PF-05089771 predicted a 1 g oral dose would be required to achieve exposures of approximately 12× Nav1.7 IC50 at maximum concentration (C max), and approximately 3× IC50 after 12 h (minimum concentration [C min] for a twice-daily regimen). Lower multiples of Nav1.7 IC50 were predicted with the same oral doses of PF-05150122, PF-05186462, and PF-05241328. In a subsequent single ascending oral dose clinical study, the predictions for PF-05089771 compared well with observed data. Based on the human PK data obtained from the microdose study and subsequent modelling, PF-05089771 provided the best opportunity to explore Nav1.7 blockade for the treatment of acute or chronic pain conditions.

Rationale The RECIST guideline defines four categories of response to treatment for cancer patients according to post-baseline changes in tumor burden, hence ignoring disease history. However, if left untreated, tumors grow exponentially, implying that pretreatment changes in tumor size are key to thoroughly assess efficacy. We present a model-based approach to estimate the rates of changes in tumor mass, before and after treatment onset. Methods Sixty-eight patients were eligible for the analysis of tumor size data from a Phase 1 study evaluating the effect of emactuzumab. In addition to tumor size measured at baseline and every six weeks during treatment, a pre-baseline measurement was gathered for each patient. A longitudinal regression model was used to estimate the rates of tumor size change before and after treatment onset. Results The median pre-treatment tumor growth exponential rate was equal to 0.022 month-1, corresponding to a tumor size doubling time of 4 months, and the on-treatment median tumor shrinkage exponential rate was equal to 0.001 month-1. Among sixteen patients categorized as stable disease per RECIST, only five had similar slopes before and after treatment while nine actually improved. One patient in particular had a therapeutically induced stabilization of the disease. Conclusion Our analysis emphasizes the importance of collecting pre-baseline scans to distinguish therapeutically induced stable disease from cases where the tumor growth is not perturbed by treatment.

Alectinib is a selective anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive non-small cell lung cancer. Alectinib and its major active metabolite M4 exhibited drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C8 in vitro. Clinical relevance of the DDI risk was investigated as part of a rapid development program to fulfill the breakthrough therapy designation. Therefore, a strategy with a combination of physiologically based pharmacokinetic (PBPK) modeling and limited clinical trials focused on generating informative data for modeling was made to ensure extrapolation ability of DDI risk. The PBPK modeling has provided mechanistic insight into the low victim DDI risk of alectinib through CYP3A4 by a novel two-dimensional analysis for fmCYP3A4 and FG , and demonstrated negligible CYPs 2C8 and 3A4 enzyme-modulating effects at clinically relevant exposure. This work supports that alectinib can be prescribed without dose adjustment for CYP-mediated DDI liabilities.

1. Alectinib is a highly selective, central nervous system-active small molecule anaplastic lymphoma kinase inhibitor. 2. The absolute bioavailability, metabolism, excretion and pharmacokinetics of alectinib were studied in a two-period single-sequence crossover study. A 50 μg radiolabelled intravenous microdose of alectinib was co-administered with a single 600 mg oral dose of alectinib in the first period, and a single 600 mg/67 μCi oral dose of radiolabelled alectinib was administered in the second period to six healthy male subjects. 3. The absolute bioavailability of alectinib was moderate at 36.9%. Geometric mean clearance was 34.5 L/h, volume of distribution was 475 L and the hepatic extraction ratio was low (0.14). 4. Near-complete recovery of administered radioactivity was achieved within 168 h post-dose (98.2%) with excretion predominantly in faeces (97.8%) and negligible excretion in urine (0.456%). Alectinib and its major active metabolite, M4, were the main components in plasma, accounting for 76% of total plasma radioactivity. In faeces, 84% of dose was excreted as unchanged alectinib with metabolites M4, M1a/b and M6 contributing to 5.8%, 7.2% and 0.2% of dose, respectively. 5. This novel study design characterised the full absorption, distribution, metabolism and excretion properties in each subject, providing insight into alectinib absorption and disposition in humans.

The efficacy and safety of alectinib, a central nervous system-active and selective anaplastic lymphoma kinase (ALK) inhibitor, has been demonstrated in patients with ALK-positive (ALK+) non-small cell lung cancer (NSCLC) progressing on crizotinib. Alectinib is mainly metabolized by cytochrome P450 3A (CYP3A) to a major similarly active metabolite, M4. Alectinib and M4 show evidence of weak time-dependent inhibition and small induction of CYP3A in vitro. We present results from 3 fixed-sequence studies evaluating drug-drug interactions for alectinib through CYP3A. Studies NP28990 and NP29042 enrolled 17 and 24 healthy subjects, respectively, and investigated potent CYP3A inhibition with posaconazole and potent CYP3A induction through rifampin, respectively, on the single oral dose pharmacokinetics (PK) of alectinib. A substudy of the global phase 2 NP28673 study enrolled 15 patients with ALK+ NSCLC to determine the effect of multiple doses of alectinib on the single oral dose PK of midazolam, a sensitive substrate of CYP3A. Potent CYP3A inhibition or induction resulted in only minor effects on the combined exposure of alectinib and M4. Multiple doses of alectinib did not influence midazolam exposure. These results suggest that dose adjustments may not be needed when alectinib is coadministered with CYP3A inhibitors or inducers or for coadministered CYP3A substrates.

Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

e20598Background: Alectinib, a potent and selective CNS-active ALK inhibitor, which has received FDA accelerated approval for treatment of patients with ALK+ NSCLC who have progressed on, or are in...

PF-184298 ((S)-2,3-dichloro-N-isobutyl-N-pyrrolidin-3-ylbenzamide) and PF-4776548 ((3-(4-fluoro-2-methoxy-benzyl)-7-hydroxy-8,9-dihydro-3H,7H-pyrrolo[2,3-c][1,7]naphthyridin-6-one)) are novel compounds which were selected to progress to human studies. Discordant human pharmacokinetic predictions arose from pre-clinical in vivo studies in rat and dog, and from human in vitro studies, resulting in a clearance prediction range of 3 to >20 mL min⁻¹ kg⁻¹ for PF-184298, and 5 to >20 mL min⁻¹ kg⁻¹ for PF-4776548. A package of work to investigate the discordance for PF-184298 is described. Although ultimately complementary to the human pharmacokinetic data in characterising the disposition of PF-184298 in humans, these data did not provide any further confidence in pharmacokinetic prediction. A fit for purpose human pharmacokinetic study was conducted for each compound, with an oral pharmacologically active dose for PF-184298, and an intravenous and oral microdose for PF-4776548. This provided a relatively low cost, clear decision making approach, resulting in the termination of PF-4776548 and further progression of PF-184298. A retrospective analysis of the data showed that, if the tools had been available at the time, the pharmacokinetics of PF-184298 in human could have been predicted from a population based simulation tool in combination with physicochemical properties and in vitro human intrinsic clearance.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug–drug interactions (DDIs). • Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme Ki, inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS • Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, fm) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug–drug interactions prior to clinical investigation. • The current study supported the use of SIMCYP®, a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug–drug interactions. AIMS The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODS The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (fm); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug–drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP®. RESULTS When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONS The current study demonstrated that the use of rhCYPs with SIMCYP® provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.