Oncology › Non-Small Cell Lung Cancer

AI-Driven Benefit-Risk Analysis for NSCLC

NSCLC accounts for 85% of all lung cancers with rapidly evolving treatment paradigms spanning EGFR/ALK-targeted therapies, PD-1/PD-L1 checkpoint inhibitors, and chemo-immunotherapy combinations. ArcaScience delivers comprehensive BRA across these complex regimens with biomarker-stratified safety profiling.

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2,200,000+

New NSCLC cases globally per year

3,400+

Active NSCLC clinical trials analyzed

$62B

Global NSCLC therapeutics market (2025)

47+

FDA-approved NSCLC therapies monitored

Disease Overview

Why NSCLC Demands Specialized BRA

Non-Small Cell Lung Cancer presents a uniquely complex BRA landscape due to rapid biomarker-driven treatment evolution, overlapping toxicity profiles from combination regimens, and accelerated approval pathways that demand robust post-marketing safety surveillance.

Biomarker-Stratified Complexity

NSCLC treatment decisions depend on EGFR, ALK, ROS1, BRAF, KRAS G12C, MET, RET, NTRK, and PD-L1 status. Each biomarker subgroup has distinct efficacy-safety profiles for osimertinib, alectinib, sotorasib, and pembrolizumab requiring subpopulation-specific BRA modeling.

Chemo-Immunotherapy Toxicity Overlap

Combinations like pembrolizumab + carboplatin/pemetrexed or nivolumab + ipilimumab create overlapping toxicities where immune-related AEs (pneumonitis, colitis) compound with chemotherapy-induced myelosuppression, requiring sophisticated causality attribution algorithms.

Accelerated Approvals & Confirmatory Data

Multiple NSCLC therapies received accelerated approval based on ORR or PFS, requiring ongoing post-marketing BRA with confirmatory OS data. Recent FDA scrutiny of accelerated approvals (e.g., atezolizumab withdrawal in certain indications) heightens the need for real-time benefit-risk monitoring.

Platform Capabilities

How ArcaScience Addresses NSCLC BRA Challenges

Our three integrated modules are configured with NSCLC-specific data, AI models trained on lung cancer safety signals, and regulatory output templates aligned with oncology submission requirements.

Data Intelligence

NSCLC Data Coverage

3,400+ NSCLC clinical trials including KEYNOTE, CheckMate, FLAURA, and ADAURA datasets. Comprehensive FAERS/EudraVigilance adverse event data for osimertinib, pembrolizumab, nivolumab, atezolizumab, sotorasib, and 40+ other NSCLC agents. Biomarker-stratified safety profiles from real-world evidence sources.

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Decision Intelligence

NSCLC-Specific AI Models

AI models trained on NSCLC-specific patterns: immune-related pneumonitis detection in lung cancer patients (where baseline respiratory compromise complicates diagnosis), EGFR TKI-associated interstitial lung disease risk stratification, and biomarker-driven subpopulation benefit-risk modeling across PD-L1 expression levels.

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Automated Outputs

NSCLC Regulatory Outputs

Submission-ready PSUR/PBRER with NSCLC-specific safety sections, Risk Management Plans addressing pneumonitis monitoring protocols, CTD Module 2.5 with OS/PFS/ORR endpoint summaries, and HEOR reports with QALY analyses relevant to NICE, G-BA, and HAS submissions for NSCLC therapies.

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NSCLC Intelligence

Platform Performance in NSCLC

8,500,000,000+

NSCLC-related adverse event data points

65%

Faster NSCLC signal detection vs. manual

Biomarker subgroups modeled simultaneously

NSCLC regulatory submissions supported

Case Evidence — NSCLC

Accelerated BRA for NSCLC Immunotherapy Combinations

Challenge

A top-10 pharma company needed to assess the benefit-risk profile of a novel PD-L1 inhibitor combined with platinum-doublet chemotherapy across PD-L1 expression subgroups (TPS ≥50%, 1-49%, <1%) for a supplemental BLA submission to the FDA.

Result

ArcaScience delivered biomarker-stratified BRA covering immune-related pneumonitis, hepatitis, and thyroid dysfunction across all PD-L1 subgroups, reducing PSUR preparation time by 58% and identifying a previously unrecognized cardiac safety signal in the PD-L1 <1% subgroup.

58%

Reduction in PSUR prep time

PD-L1 subgroups analyzed simultaneously

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ArcaScience's ability to model benefit-risk across PD-L1 subgroups gave us the confidence to include a comprehensive biomarker-stratified safety analysis in our BLA supplement. The FDA reviewers specifically noted the quality of our subpopulation analysis.

VP, Oncology Drug Safety

Top-10 Pharma Company

Frequently Asked Questions

NSCLC Benefit-Risk Analysis

Our platform tracks safety and efficacy data across sequential EGFR TKI therapies (first-line osimertinib, post-resistance amivantamab + lazertinib, and subsequent treatments). AI models analyze T790M, C797S, and MET amplification resistance patterns to provide longitudinal BRA that accounts for evolving treatment sequences and cumulative toxicity profiles across treatment lines.

Yes. NSCLC patients present a unique diagnostic challenge because immune-related pneumonitis can mimic disease progression radiographically. Our AI models are trained on differentiation patterns using clinical data, imaging correlates, biomarker trajectories, and temporal relationships to distinguish treatment-related pneumonitis from progressive disease, improving the accuracy of safety signal attribution by over 40%.

Our platform includes comprehensive data from sotorasib (CODEBREAK trials) and adagrasib (KRYSTAL trials), with AI models trained on the specific toxicity profiles of KRAS G12C inhibitors including GI toxicity, hepatotoxicity, and QTc prolongation with adagrasib. We provide comparative BRA across the KRAS G12C inhibitor class and emerging combination strategies with SHP2 inhibitors and immunotherapies.

For NSCLC programs, we generate PSUR/PBRER with oncology-specific safety sections covering irAEs, RMPs with pneumonitis and hepatitis monitoring protocols, CTD Module 2.5 with OS, PFS, ORR, and DOR endpoint summaries, and HEOR reports including QALY analyses stratified by biomarker subgroup. All outputs are formatted for FDA BLA supplements, EMA Type II variations, and PMDA partial change applications.

Yes. Our data coverage includes trastuzumab deruxtecan (T-DXd, DESTINY-Lung trials) and datopotamab deruxtecan (TROPION trials), with specific AI models for ADC-related interstitial lung disease (ILD) detection, which has emerged as a critical safety concern. The platform monitors for ILD signals with enhanced sensitivity given the high fatality rate of this ADC class effect in lung cancer patients.

See ArcaScience Applied to NSCLC

Request a demonstration of ArcaScience's platform configured for NSCLC benefit-risk analysis. Our oncology scientists will walk through biomarker-stratified data coverage, immunotherapy-specific AI models, and NSCLC regulatory output examples.

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