Gen-AI based in-silico modelling for pk-BE Studies for complex generics

Strategic Value Proposition

Context

The pharmaceutical industry faces mounting pressure to accelerate generic drug development for complex formulations while maintaining rigorous scientific standards and reducing clinical burden. Recent advances in model-informed drug development (MIDD) have positioned computational approaches as central enablers of regulatory decision-making. The U.S. FDA and European Medicines Agency (EMA) increasingly recognize physiologically-based pharmacokinetic (PBPK) modeling, in-vitro-in-vivo correlation (IVIVC), and virtual bioequivalence (VBE) as scientifically valid frameworks for establishing therapeutic equivalence.

Evidence

Contemporary research demonstrates that artificial intelligence-enhanced PBPK models achieve predictive accuracies exceeding 90% for pharmacokinetic parameter estimation, while virtual bioequivalence platforms reduce development timelines from 6-12 months to weeks and deliver cost savings of 40-70%. Machine learning integration with traditional IVIVC methodologies enables unprecedented capabilities: predictive dissolution-to-absorption modeling, patient population variability simulation, and formulation optimization with minimal experimental burden.

Complex Generics

Complex generics—encompassing orally inhaled and nasal drug products (OIDPs), extended-release systems, transdermal formulations, liposomal delivery platforms, and BCS Class IV compounds—present formidable challenges due to intricate absorption kinetics, device-dependent delivery dynamics, and formulation-sensitive performance characteristics. Conventional bioequivalence paradigms require multiple clinical studies with substantial patient exposure, creating ethical concerns and economic barriers to market entry. Mechanistic in-silico modeling addresses these limitations by establishing quantitative structure-performance relationships that link critical quality attributes (CQAs) to in-vivo bioequivalence outcomes.

Regulatory Direction

The FDA’s Generic Drug Science and Research Initiatives explicitly prioritize development of biopredictive in-vitro characterization methods coupled with virtual bioequivalence platforms, particularly for locally acting complex drug products where meaningful in-vivo data remain limited. The Center for Research on Complex Generics (CRCG) has identified model-integrated evidence as a critical pathway for demonstrating bioequivalence without exhaustive clinical testing. Current regulatory frameworks increasingly accommodate Level A IVIVC validation, mechanistic absorption modeling, and virtual trial simulations as supporting evidence for ANDA and 505(b)(2) submissions.

AI + PBPK Hybrids

Gen-Al™ Platform

KAAS BioPharma’s Gen-Al™ platform advances this scientific frontier by uniquely requiring only a single pilot in-vivo study to train product-specific algorithms—a paradigm shift that dramatically reduces patient exposure while maintaining predictive validity. This capability addresses the core limitation of existing approaches: the requirement for extensive clinical datasets to achieve model validation. By leveraging proprietary machine learning architectures that extract maximum information content from limited clinical data, our platform enables ethical, cost-effective, and scientifically rigorous bioequivalence assessment for the most challenging generic formulations.

Why do global innovators choose our in-silico Platform?

KAAS BioPharma has earned the trust of leading global pharmaceutical institutions. We successfully leverage our validated Gen-AI based in-silico modeling framework to transform dissolution and formulation data into actionable market approval strategies.

Proven AI Engine

Proprietary, product-specific algorithms validated across multiple pharmaceutical products, spanning different dosage forms and therapeutic areas.

Accellerated Timelines

with following sub-text: Achieve faster development time compared to traditional methods, enabling first-to-file and paragraph IV opportunities.

Reg-Compliances

Models are equipped with 21 CFR Part 11 compliance and align with FDA Level A IVIVC requirements, ensuring regulatory acceptance and audit readiness.

Smart Correlation Matrices

Advanced machine learning algorithms analyze 50+ in-vitro parameters—including dissolution profiles, aerodynamic particle size distribution (APSD), and formulation attributes—to construct product-specific predictive models

Virtual Bioequivalence Modeling

Proprietary GenAI-powered in-silico simulations predict pharmacokinetic outcomes and bioequivalence with 95% accuracy, reducing clinical trial requirements by >60% and enabling intelligent prototype screening.

Pilot-to-Pivotal Scaling Mechanisms

Industry-first predictive scaling algorithms extrapolate pk-BE data from pilot studies (n=12) to pivotal trial configurations (n>48) with up to 95% accuracy—transforming single pilot study data into comprehensive bioequivalence forecasts.

In-Vitro-In-Vivo Data Learning Architecture

Revolutionary Data Efficiency Through AI-Powered Intelligence Extraction. The cornerstone of our Gen-Al platform lies in its unprecedented ability to extract comprehensive bioequivalence intelligence from in-vitro and minimal pilot-scale in-vivo data—a capability that fundamentally transforms the economics and ethics of generic drug development.

Traditional Approach Limitations: Conventional bioequivalence development requires multiple sequential clinical studies: exploratory pilots, formulation refinement trials, and pivotal bioequivalence studies, each involving >12 subjects and spanning 2-4 months per iteration. This sequential testing paradigm results in:

Gen-Al Paradigm Transformation: Our platform leverages in-vitro and minimal early-phase pilot data to establish fully validated, product-specific predictive models capable of:

Generating virtual pharmacokinetic profiles for multiple prototype formulations without additional clinical testing
Predicting pivotal bioequivalence outcomes with 90-95% accuracy
Simulating population variability across virtual patients representing diverse demographics and physiological states
Forecasting 90% confidence intervals for Cmax and AUC bioequivalence metrics prior to pivotal study execution

This capability leverages advanced ensemble learning architectures that integrate:

Transfer Learning from Proprietary Database: Pre-trained neural networks incorporating pharmacokinetic patterns from numerous previously characterized products, enabling rapid model convergence from limited clinical observations.
Bayesian Optimization Framework: Adaptive algorithms that maximize information extraction from sparse in-vivo datasets, systematically identifying formulation-performance relationships with minimal experimental burden.
Mechanistic Constraint Integration: Physiologically-based absorption, distribution, and elimination models that encode first-principles pharmaceutical science, ensuring predictions remain within biologically plausible parameter spaces.
Quantifiable Impact: >70% reduction in human subject exposure compared to conventional development pathways 6-9 month acceleration in time-to-market for generic formulations 50-70% cost reduction in clinical development expenditure Elimination of 3-5 iterative clinical studies per product

This data-efficient paradigm represents a fundamental advance in ethical pharmaceutical development, aligning with the principles of Reduction and Refinement while maintaining scientific rigor and regulatory acceptability.

Ethical Optimization Through Computational Innovation

Advancing the 3R Principles in Clinical Research

Our platform reduces human testing burden by embracing the principles of Replacement, Reduction, and Refinement (3Rs), enabling safer, more informed early-phase decisions through AI-driven simulations while maintaining scientific validity.

Minimizes human subject exposure by >70% compared to conventional sequential study approaches
Enables virtual bioequivalence assessments for early-phase development candidates, reducing unnecessary clinical trials
Supports ethical clinical research frameworks by maximizing information yield from limited in-vivo observations
Facilitates informed go/no-go decisions earlier in development, preventing patient exposure to non-viable formulations

Regulatory Approach

Our computational approach aligns with FDA and EMA guidelines encouraging model-informed drug development (MIDD) and the principle of using the minimum necessary clinical data to demonstrate bioequivalence, particularly for complex generic formulations where traditional approaches may be scientifically or ethically challenging.

Cutting Edge AI Integration

This AI-powered approach represents a paradigm shift in biopharmaceutical development, combining computational efficiency with rigorous scientific validation to redefine

Intuitive Dashboard

94% Clinical Validation Accuracy

Virtual Bioequivalence Predictor with 94% clinical validation accuracy

Generated Risk Heat Maps

Formulation Risk Heatmaps highlighting critical quality attributes

Regulatory Pathway Simulator

Regulatory Pathway Simulator for 505(b)(2) strategy optimization

Cutting-Edge AI Integration

Hybrid architecture

Hybrid architecture combining Transformer networks for pattern recognition

PBPK Integration layer

Physiologically-based pharmacokinetic (PBPK) integration layer

Real time collaboration

Real-time collaboration with leading AI research partners for model updates

AI Powered Enablement

Reduce Development Cycle Time

Accelerate formulation development cycles by 6–9 months

Reduced Required PK Studies

Reduce required PK studies by 80% in early development

Audit Ready Documentation

Generate audit-ready documentation for regulatory submissions

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Contact Information

Location

A 410, Shelton Sapphire, Plot no 18 & 19, Sector 15, CBD Belapur, Navi Mumbai, Maharashtra 400614