Model‐Based Assessments of CYP‐Mediated Drug–Drug Interaction Risk of Alectinib: Physiologically Based Pharmacokinetic Modeling Supported Clinical Development

Cleary, Yumi; Gertz, Michael; Morcos, Peter N.; Yu, Li; Youdim, Kuresh A.; Phipps, Alex; Fowler, Stephen; Parrott, Neil

doi:10.1002/cpt.956

Cited by 20 publications

(24 citation statements)

References 37 publications

(64 reference statements)

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“…The effect of moderate and severe hepatic impairment on the PK of alectinib and M4 was prospectively investigated using a previously developed PBPK model . Briefly, the base alectinib model was constructed using SimCYP ® software with (1) absorption assuming a first‐order rate constant and fraction absorbed as estimated previously, (2) volume of distribution predicted by mechanistic equations of the tissue partition coefficients, and (3) clearance (CL) based on plasma clearance determined following intravenous administration in humans .…”

Section: Methodsmentioning

confidence: 99%

“…The fraction metabolized through CYP3A4 enzyme (fm CYP3A4 ) of alectinib was estimated to be ∼40% to 50% based on nonclinical hepatocyte data . A combination of in vivo fm CYP3A4 of 40% and intestinal availability (F G ) of ≥90% were directly estimated from clinical data from a DDI study with a potent CYP3A inhibitor using the PBPK model, and the corresponding intrinsic CL through CYP3A4 metabolism (CLu int,CYP3A4 ) and F G were retained in the model . Subsequently, the model was verified with clinical DDI study results with CYP3A enzyme modulators, which were not included in the model development .…”

Section: Methodsmentioning

confidence: 99%

“…A combination of in vivo fm CYP3A4 of 40% and intestinal availability (F G ) of ≥90% were directly estimated from clinical data from a DDI study with a potent CYP3A inhibitor using the PBPK model, and the corresponding intrinsic CL through CYP3A4 metabolism (CLu int,CYP3A4 ) and F G were retained in the model . Subsequently, the model was verified with clinical DDI study results with CYP3A enzyme modulators, which were not included in the model development . This alectinib base model was then used to predict the effect of hepatic impairment on alectinib PK using default SimCYP ® hepatic impairment models (Child‐Pugh A‐C) .…”

Section: Methodsmentioning

confidence: 99%

“…A minimal PBPK model was developed for the major metabolite, M4, in which the apparent volume of distribution at steady state (V ss ) and CL (including 15% of hepatic metabolism through CYP3A4 enzyme) were empirically estimated from a clinical DDI study with a potent CYP3A inhibitor . The effect of hepatic impairment in the M4 model was incorporated primarily via reduced liver size; a reduction in CYP3A abundance, the known metabolic pathway for M4 elimination; and a reduction in albumin values.…”

Section: Methodsmentioning

confidence: 99%

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Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Morcos

Cleary

Sturm-Pellanda

et al. 2018

The Journal of Clinical Pharma

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Alectinib is approved and recommended as the preferred first‐line treatment for patients with anaplastic lymphoma kinase (ALK)‐positive non–small cell lung cancer. The effect of hepatic impairment on the pharmacokinetics (PK) of alectinib was assessed with physiologically based PK modeling prospectively and in a clinical study. An open‐label study (NCT02621047) investigated a single 300‐mg dose of alectinib in moderate (n = 8) and severe (n = 8) hepatic impairment (Child‐Pugh B/C), and healthy subjects (n = 12) matched for age, sex, and body weight. Physiologically based PK modeling was conducted prospectively to inform the clinical study design and support the use of a lower dose and extended PK sampling in the study. PK parameters were calculated for alectinib, its major similarly active metabolite, M4, and the combined exposure of alectinib and M4. Unbound concentrations were assessed at 6 and 12 hours postdose. Administration of alectinib to subjects with hepatic impairment increased the area under the plasma concentration–time curve from time 0 to infinity of the combined exposure of alectinib and M4 to 136% (90% confidence interval [CI], 94.7‐196) and 176% (90%CI 98.4‐315), for moderate and severe hepatic impairment, respectively, relative to matched healthy subjects. Unbound concentrations for alectinib and M4 did not appear substantially different between hepatic‐impaired and healthy subjects. Moderate hepatic impairment had only a modest, not clinically significant effect on alectinib exposure, while the higher exposure observed in severe hepatic impairment supports a dose adjustment in this population.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Morcos

Cleary

Sturm-Pellanda

et al. 2018

The Journal of Clinical Pharma

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“…in standard of care, crizotinib‐resistant, ALK‐positive NSCLC, supporting its initial approval (Alecensa, USPI, Roche, Basel, Switzerland). During its development, the MIDD approach was judiciously implemented to derive the alectinib clinical dosing regimen in the crizotinib‐resistant population and support dosing with concomitant medications . Alectinib is now listed in the US National Comprehensive Cancer Network Guideline 2018 as a preferred treatment option for first‐line (1L) ALK‐positive NSCLC based on results from a Japanese phase III trial, J‐ALEX (JapicCTI‐132316), using alectinib 300 mg b.i.d.…”

mentioning

confidence: 99%

Model‐Informed Drug Development Approach to Expedite Approval: Case of Alectinib in First‐Line Anaplastic Lymphoma Kinase + Non‐Small Cell Lung Cancer

Morcos¹,

Liu

Blumenthal

et al. 2019

Clin Pharma and Therapeutics

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The first-line approval of alectinib provides an intriguing example of using model-informed drug development (MIDD) to expedite approval under breakthrough designation. Emerging efficacy/safety results with alectinib 300 mg twice daily in Japanese patients triggered discussions with the US Food and Drug Administration (FDA) on using quantitative clinical pharmacology analyses and supportive clinical data to support the approval of the alectinib 600 mg twice daily dosing regimen and ensure more rapid availability of alectinib to US patients.MIDD aims to use state-of-the-art pharmaceutical science methodology incorporating pharmaco/statistical models of drug efficacy and safety to facilitate more efficient drug development and decision making. 1 MIDD integrates information about the drug, the disease, and their interplay within the body and incorporates those considerations into mathematical models to quantify drug effect and clinical outcomes. These models are then applied to address clinical development questions (e.g., inform study designs, predict clinical outcomes, optimize dosing, and others) and/or support regulatory decision making (https://www.fda. gov/Drugs/NewsEvents/ucm604837.htm). The FDA MIDD Pilot Program has been established to provide an opportunity for drug developers and the FDA to discuss MIDD approaches that may assist in lowering drug attrition and aiding in situations of regulatory uncertainty. 1 These approaches may be of particular value when clinical data are limited such that integration across nontraditional sources may be needed, and MIDD can assess uncertainties about issues such as dosing, duration, and patient selection. Examples of successful implementation MIDD have emerged across the continuum of drug development and approval from exploring safety-related attrition to informing dosing recommendations of drugs in special populations. With the increasing use of quantitative clinical pharmacology (qCP) analyses in development, opportunities for implementation of MIDD continue to emerge to address important development and regulatory questions, with the goal of bringing effective medicines to patients sooner.Alectinib is a small molecule tyrosine kinase inhibitor, which potently and selectively inhibits the anaplastic lymphoma kinase (ALK), a known oncogenic driver in non-small cell lung cancer (NSCLC), with a half-maximal inhibitory concentration of 1.9 nM in enzymatic assays. Two single-arm phase II trials demonstrated efficacy with acceptable safety of alectinib 600 mg b.i.d. in standard of care, crizotinib-resistant, ALK-positive NSCLC, supporting its initial approval (Alecensa, USPI, Roche, Basel, Switzerland). During its development, the MIDD approach was judiciously implemented to derive the alectinib clinical dosing regimen in the crizotinibresistant population and support dosing with concomitant medications. 2,3 Alectinib is now listed in the US National Comprehensive Cancer Network Guideline 2018 as a preferred treatment option for first-line (1L) ALK-positive NSCLC based on...

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Physiologically‐based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

Alsmadi

Aldaoud

Jaradat

et al. 2021

Biopharm & Drug Disp

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Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically‐based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model‐predicted plasma exposures in patients with different health conditions within average 2‐fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non‐small cell lung cancer, moderate HIP, and severe HIP at 1‐, 1.5‐, and 1.8‐fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.

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Model‐Based Assessments of CYP‐Mediated Drug–Drug Interaction Risk of Alectinib: Physiologically Based Pharmacokinetic Modeling Supported Clinical Development

Cited by 20 publications

References 37 publications

Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Model‐Informed Drug Development Approach to Expedite Approval: Case of Alectinib in First‐Line Anaplastic Lymphoma Kinase + Non‐Small Cell Lung Cancer

Physiologically‐based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

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