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

Abstract

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...

Pharmacometric analyses of alectinib to facilitate approval of the optimal dose for the first‐line treatment of anaplastic lymphoma kinase–positive non‐small cell lung cancer

“…To avoid immortal time bias, a landmark analysis was conducted using a PK exposure parameter before any PFS event occurred (i.e., average molar concentration of both alectinib and M4 during the first 6 weeks of treatment [C average_6 week ]). 25 , 26 , 27 In three consecutive CPH analyses, conclusions were consistent, and the same C average_6 week threshold value of 1040 nmol/L was identified. In patients with C average_6 week above this threshold, the risk of progression was reduced by about 40% compared with those with C average_6 week below this threshold.…”

“…Pharmacometrics played a critical role in supporting alectinib 600 mg b.i.d. as an effective, well‐tolerated optimal dose regimen in treatment‐naïve patient populations 27 . Based on population PK analyses, the PK characteristics of alectinib and M4 were confirmed to be consistent across age, sex, race, treatment lines, and disease status when body weight is taken into consideration.…”

“…Estimated individual PK parameters were used to investigate the PK exposure–PFS relationship starting with J‐ALEX data and sequentially adding ALEX and ALESIA data. To avoid immortal time bias, a landmark analysis was conducted using a PK exposure parameter before any PFS event occurred (i.e., average molar concentration of both alectinib and M4 during the first 6 weeks of treatment [C average_6 week ]) 25–27 . In three consecutive CPH analyses, conclusions were consistent, and the same C average_6 week threshold value of 1040 nmol/L was identified.…”

“…Initial investigation of whether variability in alectinib exposure could partly explain variability in efficacy was conducted using data from J‐ALEX, a phase III study of alectinib in patients with ALK ‐positive NSCLC. To avoid immortal time bias, a landmark CPH analysis was performed to characterize the relationship between alectinib exposure and PFS 25–27 . Individual C average _6 week , defined as the cumulative area under the molar concentration curve of both alectinib and M4 for the first 6 weeks on treatment derived from population PK models divided by 6 weeks, was used as a surrogate for exposure for each patient.…”

“…To avoid immortal time bias, a landmark CPH analysis was performed to characterize the relationship between alectinib exposure and PFS. 25 , 26 , 27 Individual C average _6 week , defined as the cumulative area under the molar concentration curve of both alectinib and M4 for the first 6 weeks on treatment derived from population PK models divided by 6 weeks, was used as a surrogate for exposure for each patient. This time frame was selected as no patient in the alectinib treatment arm progressed during the first 6 weeks.…”