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QSAR & Regulatory Toxicology

QSAR Toxicology & ICH M7 Impurity Assessment

Global Regulatory Toxicology Solutions for Pharmaceutical, Biotechnology & API Manufacturers

QSAR Toxicology & ICH M7 Impurity Assessment

RASA Life Science Informatics provides comprehensive QSAR (Quantitative Structure–Activity Relationship) modeling, computational toxicology, impurity qualification, and ICH M7-compliant mutagenic risk assessment services for pharmaceutical companies, biotechnology organizations, API manufacturers, CDMOs, CROs, and regulatory affairs teams worldwide.

Our scientific experts combine computational toxicology, cheminformatics, regulatory science, and AI-driven predictive modeling to support impurity qualification, genotoxic impurity assessment, nitrosamine risk evaluation, extractables and leachables studies, and regulatory submissions across global markets.

Service Workflow

ICH M7 Mutagenic Impurity Assessment

A structured computational toxicology workflow to identify, evaluate, and classify mutagenic impurities in drug substances.

01

Structural Alert Identification

Advanced screening of chemical structures to identify molecular alerts associated with DNA reactivity.

02

Dual QSAR Assessment

Parallel evaluation using complementary expert rule-based and machine-learning statistical models.

03

Weight-of-Evidence & Expert Review

Rigorous scientific analysis to resolve conflicting predictions and finalize toxicological safety conclusions.

04

Impurity Classification (Class 1–5)

Categorization of impurities according to the established ICH M7 toxicity classes.

05

TTC-Based Risk Assessment

Determining Threshold of Toxicological Concern (TTC) exposure levels and acceptable daily intake limits.

06

Control Strategy & Support

Formulating audit-ready regulatory reports and mitigation strategies for submission packages.

Classification

ICH M7 Impurity Classification

A structured grouping of impurities to determine appropriate threshold limits for toxicological control.

1

Class 1

Known mutagenic carcinogens.

2

Class 2

Known mutagens with unknown carcinogenicity.

3

Class 3

Alerting structures structurally unrelated to the API.

4

Class 4

Alerting structures with evidence of non-mutagenicity.

5

Class 5

No structural alerts or sufficient evidence of safety.

Endpoints

QSAR Toxicology Endpoints Evaluated

Key endpoints assessed through our computational pipelines to establish chemical and drug safety profiles.

Genotoxicity & Mutagenicity

  • Ames Mutagenicity Alert
  • In Vitro Genotoxicity
  • In Vivo Genotoxicity

Systemic & Organ Toxicity

  • Hepatotoxicity Assessment
  • Organ-Specific Toxicity
  • Endocrine Disruption Screening

Developmental & Skin

  • Developmental Toxicity
  • Reproductive Toxicity
  • Skin Sensitization

Environmental & Other

  • Carcinogenicity Potential
  • Ecotoxicity Screening
  • Aquatic & Soil Toxicity
Methodology

Dual QSAR Assessment Strategy

Our dual-methodology framework utilizes parallel prediction models to increase accuracy and meet regulatory requirements.

Rule-Based Modeling

Expert knowledge-based systems (e.g., Derek Nexus, Toxtree) that scan chemical structures to identify molecular alerts associated with specific toxicological reactivities and endpoints.

Structural Alerts Organic Chemistry Rules Mechanistic Explanations

Statistical-Based Modeling

Machine learning algorithms (e.g., Leadscope, Sarah Nexus) trained on large, standardized datasets that predict toxicological outcomes by quantifying structural similarities.

Machine Learning Similarity Metrics Quantitative Predictions
Technology Suite

Supported Toxicology Platforms

Our workflows integrate industry-leading computational platforms and algorithms to deliver robust, compliant assessments.

Regulatory QSAR

OQ
OECD QSAR Toolbox Standard for chemical hazard screening and read-across.
VE
VEGA Consensus predictions based on international data sets.
TO
Toxtree Rule-based decision tree alerts for toxic hazards.
LA
Lazar Statistical models with clear explanation paths.
DS
Danish (Q)SAR DB Toxicity predictions for 600k+ chemicals.

Commercial QSAR

LE
Leadscope Integrated databases and model suites for compliance.
CU
CASE Ultra Optimized statistical modeling for mutagenicity.
DN
Derek Nexus Rule-based expert system for predicting toxicity.
SN
Sarah Nexus Expert-validated statistical Ames mutagenicity model.

AI & Frameworks

ML
Deep Learning Custom neural network models trained on chemical sets.
RA
Read-Across Endpoint filling using chemically grouped analog data.
SS
Similarity Analysis Molecular fingerprint comparisons for risk mapping.
PT
Predictive Pipelines High-throughput automated toxicology workflows.
Specialized Assessments

Computational Risk & Impurity Assessments

Parallel expert workflows for identifying, quantifying, and mitigating toxicological risks in drug substances and packaging systems.

Nitrosamine Risk Assessment

Scope of Evaluation

  • Nitrosamine Impurities & NDSRIs
  • Process-Generated Nitrosamines
  • Degradation-Related Nitrosamines

Deliverables Include

  • Molecular Structure Assessment
  • Mutagenicity & Potency Categorization
  • Acceptable Daily Intake (AI) Calculation
  • Regulatory Justification Reports

Extractables & Leachables

Materials Supported

  • Packaging Materials & Container Closures
  • Single-Use Manufacturing Systems
  • Process Equipment Components

Assessment Parameters

  • Chemical Identification Review
  • TTC (Threshold of Toxicological Concern)
  • PDE (Permitted Daily Exposure) Derivation
  • Safety Margin & Regulatory Risk Reporting
Compliance

Regulatory Frameworks We Support

Our analyses are designed to meet or exceed toxicological guidelines established by major international regulatory agencies.

ICH Guidelines
US FDA Regulations
EMA Requirements
MHRA Expectations
PMDA Standards
WHO Guidelines
Global GMP Compliance
Submissions

Regulatory Documentation Support

Audit-ready documentation prepared in standard formats to support global regulatory filings.

Pharmaceutical Submissions

IND NDA ANDA DMF CEP Marketing Authorization (MAA)

Global Regulatory Agencies

US FDA EMA MHRA PMDA TGA Health Canada
Deliverables

Deliverables

ICH M7 Reports

✓Impurity classification
✓QSAR summaries
✓Structural alert evaluation
✓Expert toxicological review
✓TTC calculations
✓Regulatory conclusions

Toxicological Assessment Reports

✓Endpoint-specific evaluations
✓Read-across assessments
✓Risk characterization
✓Safety justification

Regulatory Submission Packages

✓Publication-quality reports
✓Auditor-ready documentation
✓Regulatory response support
Sectors

Industries We Serve

We deliver customized computational toxicology solutions and regulatory packages to a wide range of global sectors.

Pharmaceutical Companies

Comprehensive impurity qualification, nitrosamine risk assessments, and compliance reports to support global filings.

API Manufacturers

Rapid process impurity screening, structural alert analysis, and mutagenic risk evaluation profiles.

Biotechnology Organizations

Computational toxicity profiles for early-stage screening of novel molecules and therapeutic candidates.

CROs & CDMOs

Independent, high-throughput computational toxicology reviews to augment external research services.

Medical Device & Packaging

Extractables and leachables (E&L) evaluations, container closure analyses, and material safety documentation.

Chemical & Specialty Materials

In silico hazard profiling, ecotoxicity screenings, and occupational safety endpoint predictions.

Why RASA

Why Choose RASA for QSAR & ICH M7 Assessment?

Regulatory Expertise

Deep understanding of ICH M7, FDA, EMA, and OECD toxicology frameworks.

Global Compliance

Support for submissions across North America, Europe, Asia-Pacific, and emerging markets.

Scientific Excellence

Integrated computational toxicology, cheminformatics, and regulatory science expertise.

AI-Powered Toxicology

Advanced predictive models supporting faster, more accurate toxicological evaluation.

Confidential & Secure

Strict confidentiality and secure handling of proprietary chemical structures and data.

Service FAQ

Frequently Asked Questions

Quantitative Structure–Activity Relationship (QSAR) modeling is a computational approach that predicts the biological activity, toxicity, and physicochemical properties of chemical compounds based on their molecular structure. QSAR models help researchers evaluate compound safety and efficacy before experimental testing, reducing development costs and accelerating drug discovery.
QSAR is widely used in pharmaceutical research, chemical safety assessment, regulatory submissions, impurity evaluation, and environmental risk assessment.

RASA Life Science Informatics offers comprehensive computational toxicology and regulatory assessment services, including:
QSAR Modeling
Mutagenicity Prediction
Carcinogenicity Assessment
Skin Sensitization Analysis
Genotoxicity Evaluation
Reproductive Toxicity Prediction
Developmental Toxicity Assessment
Environmental Toxicity Prediction
Endocrine Disruption Screening
ADMET Prediction
Regulatory Risk Assessment
ICH M7 Compliance Support
Nitrosamine Risk Assessment
Our workflows support pharmaceutical companies, biotechnology organizations, CROs, chemical manufacturers, and regulatory research programs.

Computational Toxicology uses bioinformatics, cheminformatics, machine learning, and predictive modeling to evaluate the safety profile of chemicals, pharmaceutical compounds, impurities, and metabolites.
By predicting toxicological endpoints before laboratory testing, computational toxicology helps organizations:
Reduce experimental costs
Accelerate safety assessments
Support regulatory submissions
Prioritize safer compounds
Improve decision-making in drug development

QSAR Modeling establishes mathematical relationships between chemical structure and biological activity. These models use molecular descriptors, fingerprints, and machine learning algorithms to predict:
Toxicity
Mutagenicity
Carcinogenicity
Bioavailability
Environmental Impact
Drug-Likeness
QSAR models are recognized by regulatory agencies worldwide and are commonly used in pharmaceutical and chemical safety evaluations.

Mutagenicity assessment evaluates the likelihood that a chemical compound may cause genetic mutations. Computational mutagenicity prediction is particularly important for pharmaceutical impurities, degradation products, metabolites, and new chemical entities.
These analyses help support regulatory compliance and early-stage safety evaluation.

Carcinogenicity assessment predicts whether a compound has the potential to contribute to cancer development through long-term exposure. Computational carcinogenicity models help researchers identify high-risk compounds before advancing them into costly experimental studies.

Skin sensitization analysis evaluates the potential of a compound to trigger allergic skin reactions after repeated exposure. This assessment is widely used in pharmaceutical development, cosmetics research, personal care products, and chemical safety programs.

Environmental toxicity prediction evaluates the potential ecological impact of chemicals and pharmaceutical compounds on aquatic and terrestrial ecosystems.
Common endpoints include:
Aquatic Toxicity
Fish Toxicity
Algae Toxicity
Daphnia Toxicity
Bioaccumulation Potential
Environmental Persistence
These analyses support environmental risk assessments and regulatory submissions.

Yes. RASA provides comprehensive ICH M7 compliance support for pharmaceutical organizations.
Our services include:
ICH M7 Impurity Evaluation
Mutagenic Impurity Assessment
Structure–Activity Relationship Analysis
Nitrosamine Risk Assessment
Regulatory Toxicological Assessment
Impurity Qualification Support
Expert Review Documentation
These assessments help pharmaceutical companies meet global regulatory requirements.

ICH M7 is an internationally recognized regulatory guideline that addresses the assessment and control of DNA-reactive (mutagenic) impurities in pharmaceutical products. The guideline recommends the use of computational toxicology methods, including QSAR modeling, to evaluate impurity-related mutagenic risks.
ICH M7 compliance is required by many regulatory agencies worldwide, including the FDA, EMA, MHRA, and PMDA.

Nitrosamine Risk Assessment evaluates the potential presence and toxicity of nitrosamine impurities in pharmaceutical products. Due to recent global regulatory concerns, nitrosamine evaluation has become a critical component of pharmaceutical quality and safety programs.
Our workflows support:
Nitrosamine Identification
Risk Ranking
Structural Assessment
Regulatory Documentation
Toxicological Evaluation

ADMET stands for:
Absorption
Distribution
Metabolism
Excretion
Toxicity
ADMET prediction helps researchers evaluate drug-like properties and safety risks early in the discovery process. Computational ADMET assessment can significantly reduce late-stage failures by identifying compounds with unfavorable pharmacokinetic or toxicological profiles.

Our toxicology workflows utilize industry-leading platforms including:
Regulatory Toxicology
OECD QSAR Toolbox
VEGA
Toxtree
Lazar
ADMET Prediction
SwissADME
pkCSM
ADMETlab
ProTox-II
Cheminformatics
RDKit
Open Babel
KNIME
Machine Learning & AI
Scikit-Learn
TensorFlow
DeepChem

We commonly work with:
Chemical Structures (SMILES)
SDF Files
MOL Files
Drug Candidates
Pharmaceutical Impurities
Metabolites
Degradation Products
Environmental Chemicals
Our team can assist in preparing and standardizing chemical structures before analysis.

Typical project deliverables include:
QSAR Prediction Reports
Mutagenicity Assessments
Carcinogenicity Evaluations
Skin Sensitization Reports
ADMET Profiles
Environmental Toxicity Reports
ICH M7 Assessment Reports
Nitrosamine Risk Assessments
Regulatory Documentation Support
Publication-Ready Figures
Comprehensive Scientific Reports
Deliverables can be customized according to project objectives and regulatory requirements.

Our services support:
Pharmaceutical Companies
Biotechnology Organizations
CROs
Chemical Manufacturers
Cosmetic Companies
Agrochemical Companies
Regulatory Research Programs
Environmental Safety Organizations
These industries rely on predictive toxicology to improve safety assessments and accelerate regulatory decision-making.

RASA combines expertise in computational toxicology, cheminformatics, regulatory science, machine learning, and drug discovery to deliver scientifically rigorous and regulatory-ready toxicological assessments. Our workflows support global regulatory compliance, early safety evaluation, impurity qualification, and risk assessment programs while reducing reliance on costly experimental testing.
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