The global biosimulation market is projected to grow from USD 4.27 billion in 2026 to USD 9.24 billion by 2031, registering a robust CAGR of 16.7% during the forecast period. The market is experiencing significant momentum as pharmaceutical and biotechnology companies increasingly adopt computational modeling and simulation technologies to improve the efficiency, accuracy, and success rates of drug development programs.
As drug discovery becomes more complex and development costs continue to rise, organizations are turning to biosimulation platforms to predict drug behaviour, optimize dosing strategies, design more efficient clinical trials, and support regulatory submissions. Technologies such as physiologically based pharmacokinetic (PBPK) modeling, quantitative systems pharmacology (QSP), population PK/PD modeling, and model-informed drug development (MIDD) are becoming essential tools across the pharmaceutical R&D ecosystem.
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What Is Driving the Biosimulation Market?
The traditional drug development process is expensive, time-consuming, and associated with high failure rates, particularly during clinical trials. Biosimulation addresses these challenges by enabling researchers to conduct virtual experiments, simulate biological responses, and evaluate therapeutic outcomes before advancing to costly human studies.
Key growth drivers include:
- Increasing complexity of drug discovery and development
- Rising pharmaceutical and biotechnology R&D expenditure
- Growing focus on improving clinical trial success rates
- Expanding adoption of precision medicine approaches
- Greater use of virtual patient and disease modeling
- Increasing reliance on data-driven decision-making in drug development
Biosimulation is now widely used not only by pharmaceutical and biotechnology companies, but also by contract research organizations (CROs), academic research institutions, and regulatory agencies seeking to accelerate innovation while reducing development risk.
Regulatory Support Is Accelerating Adoption
One of the strongest catalysts for market growth is the increasing acceptance of biosimulation by global regulatory authorities.
The US Food and Drug Administration (FDA) has highlighted the extensive use of PBPK modeling in regulatory submissions to:
- Predict drug-drug interactions
- Support dosing recommendations
- Evaluate special patient populations
- Reduce the need for certain clinical studies
In addition, the International Council for Harmonisation (ICH) introduced the M15 guideline on Model-Informed Drug Development (MIDD), which promotes the harmonization and broader adoption of modeling approaches across global regulatory frameworks.
These developments are creating substantial demand for advanced biosimulation platforms capable of integrating biological, pharmacological, and clinical data into unified predictive environments.
Core Technologies Reshaping Drug Development
Physiologically Based Pharmacokinetic (PBPK) Modeling
PBPK models simulate how drugs are absorbed, distributed, metabolized, and eliminated within the human body. These models help researchers predict clinical outcomes across different patient populations and support regulatory decision-making.
Quantitative Systems Pharmacology (QSP)
QSP combines systems biology with pharmacology to model complex disease mechanisms and drug interactions. It is increasingly used in oncology, immunology, and rare disease research to identify optimal therapeutic strategies.
Population PK/PD Modeling
Population pharmacokinetic/pharmacodynamic modeling evaluates variability in drug response among patient groups, enabling more precise dose selection and individualized treatment approaches.
Model-Informed Drug Development (MIDD)
MIDD integrates diverse modeling approaches throughout the drug development lifecycle, helping sponsors make better decisions regarding candidate selection, trial design, dose optimization, and regulatory strategy.
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AI Is Emerging as a Major Competitive Differentiator
Artificial intelligence is becoming increasingly intertwined with biosimulation workflows. AI technologies are being used to:
- Automate model development
- Improve parameter estimation
- Enhance predictive accuracy
- Identify hidden biological patterns
- Accelerate simulation workflows
- Support virtual patient generation
The convergence of AI, machine learning, and mechanistic biosimulation is expected to significantly expand the capabilities of predictive drug development over the next five years.
Recent Strategic Developments
Certara and Altasciences Partnership
In May 2026, Certara and Altasciences announced a strategic partnership designed to accelerate early drug development. The collaboration combines Certara’s biosimulation and regulatory science expertise with Altasciences’ preclinical and clinical development capabilities, enabling sponsors to optimize decision-making and reduce development risk.
Simulations Plus AI Collaboration Programs
In March 2026, Simulations Plus launched strategic collaboration programs focused on AI-enabled modeling. These initiatives aim to integrate artificial intelligence with biosimulation workflows to improve model development, enhance predictive performance, and accelerate drug development decisions.
Schrödinger and Eli Lilly Collaboration
In January 2026, Schrödinger partnered with Eli Lilly to provide access to its TuneLab platform within the LiveDesign environment. The collaboration supports advanced protein engineering and molecular design within a unified digital workspace, helping accelerate therapeutic discovery and optimization.
Competitive Landscape
The biosimulation market includes a mix of established software providers and specialized modeling companies that support pharmaceutical and biotechnology R&D through predictive simulation technologies.
Leading companies include:
- Certara (US)
- Dassault Systèmes (France)
- Schrödinger, Inc. (US)
- Simulations Plus (US)
- Advanced Chemistry Development, Inc. (Revvity) (Canada)
- Chemical Computing Group
- Rosa & Co.
- Genedata AG (Danaher)
- Physiomics plc
- In Silico Biosciences
- OpenEye Cadence Molecular Sciences
- Insilico Medicine
- Metrum Research Group
These companies compete across multiple domains, including PBPK modeling, QSP, pharmacometrics, molecular modeling, computational chemistry, predictive toxicology, and virtual patient simulation.
Company Spotlight: Certara
Certara has established a particularly strong position in the biosimulation market through its broad portfolio of model-based drug development solutions. The company supports pharmaceutical, biotechnology, and regulatory organizations in:
- Dose selection
- Safety and toxicity prediction
- Efficacy assessment
- Clinical trial optimization
- Regulatory submission preparation
A notable recent development was the introduction of an AI-based QSP platform in October 2025, designed to automate model generation, improve predictive performance, and enable researchers to analyze complex biological systems more efficiently.
By combining PBPK, QSP, regulatory science, and AI capabilities, Certara continues to strengthen its leadership position within the biosimulation ecosystem.
Dassault Systèmes: Virtual Twins for Life Sciences
Dassault Systèmes leverages its 3DEXPERIENCE platform and Life Sciences Solutions Portfolio to provide advanced biosimulation and virtual twin technologies for pharmaceutical and healthcare organizations.
Its solutions enable customers to:
- Model biological systems
- Conduct virtual experiments
- Simulate disease progression
- Predict therapeutic outcomes
- Improve lifecycle decision-making
The company is also expanding its capabilities through cloud-based collaboration, AI integration, and virtual twin technology, positioning itself as a major player in the digital transformation of life sciences.
Schrödinger: Combining Physics-Based Modeling and AI
Schrödinger has built a differentiated platform that combines:
- Molecular modeling
- Computational chemistry
- Machine learning
- Predictive simulation
This integrated approach allows pharmaceutical and biotechnology companies to accelerate target identification, lead optimization, and candidate selection while improving the probability of clinical success.
Its ongoing investments in AI-driven molecular design and protein engineering are helping expand the role of biosimulation beyond traditional pharmacokinetics into earlier stages of drug discovery.
Challenges Limiting Market Expansion
Despite its strong growth trajectory, the biosimulation market faces several important challenges.
High implementation costs
Advanced biosimulation platforms often require significant investment in software licenses, computational infrastructure, and specialized personnel.
Shortage of skilled professionals
There is a limited global pool of experts with expertise in pharmacometrics, systems pharmacology, computational biology, and regulatory modeling, which can slow adoption.
Model validation and regulatory concerns
Ensuring that biosimulation models are scientifically robust, reproducible, and acceptable to regulators remains a critical challenge, particularly for complex QSP and virtual patient models.
Data integration complexities
Integrating omics data, preclinical findings, clinical trial data, and real-world evidence into unified simulation environments requires sophisticated data management and interoperability capabilities.
Lower awareness among smaller organizations
Many small and mid-sized pharmaceutical and biotechnology companies still have limited awareness of the potential return on investment offered by biosimulation technologies.
Emerging Opportunities
Precision Medicine
As healthcare moves toward personalized therapies, biosimulation will play a crucial role in predicting individual patient responses and optimizing treatment strategies.
Rare Disease Research
Virtual modeling approaches can help overcome the challenges associated with small patient populations and limited clinical data in rare disease development.
Cell and Gene Therapies
Biosimulation is increasingly being explored for gene therapy, cell therapy, and RNA-based therapeutics, where traditional development paradigms are often insufficient.
Real-World Evidence Integration
The incorporation of electronic health records, wearable device data, and other real-world evidence sources into biosimulation platforms could significantly enhance predictive accuracy and post-market decision-making.
Market Outlook Through 2031
The biosimulation market is entering a phase of rapid expansion driven by the convergence of:
- Mechanistic biological modeling
- Artificial intelligence
- Cloud computing
- Digital twin technology
- Advanced analytics
- Regulatory acceptance of MIDD approaches
As pharmaceutical companies face increasing pressure to reduce development timelines, lower R&D costs, and improve clinical success rates, biosimulation is evolving from a specialized research tool into a core strategic capability across the drug development lifecycle.
The projected growth from USD 4.27 billion in 2026 to USD 9.24 billion by 2031 reflects not only rising adoption of PBPK, QSP, and pharmacometric modeling, but also the broader transformation of the life sciences industry toward predictive, data-driven, and AI-enabled drug development.
Conclusion
Biosimulation is fundamentally changing how new medicines are discovered, developed, and evaluated. By enabling researchers to simulate complex biological processes, predict drug behaviour, optimize clinical trial designs, and support regulatory decisions, biosimulation technologies are helping the pharmaceutical industry address some of its most pressing challenges.
With strong regulatory support, accelerating AI integration, expanding applications across precision medicine and advanced therapeutics, and continued innovation from leading companies such as Certara, Dassault Systèmes, Schrödinger, Simulations Plus, and Revvity, the biosimulation market is poised for substantial long-term growth.
Organizations that invest early in integrated, AI-enhanced biosimulation platforms will be better positioned to improve R&D productivity, reduce development risk, and bring innovative therapies to patients faster in an increasingly competitive global pharmaceutical landscape.
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