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In silico and predictive modeling services for drug development
Predictive modeling technologies to accelerate formulation, reduce risk, and support data-driven development decisions
In silico and predictive modeling are transforming drug development and manufacturing by enhancing efficiency, accuracy to allow innovations. Powered by artificial intelligence, machine learning, bioinformatics, molecular dynamics simulations, and systems biology, these tools help accelerate the development of new drugs, optimize their formulation, ensure quality in manufacturing, and streamline regulatory processes, all while potentially reducing costs and time to market.
Thermo Fisher Scientific integrates predictive modeling, ADME-PK expertise, and oral solid dose development knowledge to support decision-making across formulation, process development, and clinical strategy.
What our predictive modeling services include
Our predictive modeling services combine in silico modeling, PBPK analysis, and AI-enabled tools to support formulation, process development, and clinical decision-making across drug development.
OSDPredict is Thermo Fisher Scientific’s AI/ML powered digital toolbox that helps biotech innovators solve formulation challenges before they become costly setbacks.
By combining multiple predictive models, including Thermo Fisher Scientific’s Quadrant 2™ platform, OSDPredict delivers data-driven foresight into solubility, permeability, bioavailability, FIH dosing, packaging, and scale-up planning.
Key applications of OSDPredict
- Formulation behavior prediction—anticipate solubility, permeability, and bioavailability issues before they derail programs
- First-in-human dose optimization—model FIH dosing with confidence and clarity
- Packaging selection—identify the right packaging strategy to ensure stability and compliance
- Regulatory data generation—support IND-enabling studies with predictive insight
- Scale-up planning—plan manufacturing transitions earlier with higher probability of success
What are the benefits of using OSDPredict?
- Save precious API—minimize experimental waste and conserve scarce molecules.
- Rapid advancement of early development—reach milestones faster with predictive foresight
- De-risk decision making—act with confidence, guided by AI-driven predictions
- Increase first-time success—reduce reformulation cycles and achieve IND readiness smoothly
Between 70% and 90% of new chemical entities in development face solubility challenges, affecting bioavailability. Addressing these issues requires knowledge of delivery mechanisms and excipient functionality. Early consideration of formulation strategies affecting bioavailability and solubility is important to avoid costly errors later.
Quadrant 2
Thermo Fisher Scientific’s proprietary Quadrant 2 platform is an early diagnostic tool for solubility and bioavailability enhancement. By providing in silico predictions of formulation performance, it helps reduce development time and costs compared with trial-and-error approaches
Helpful resources
Browse our resource library to learn more about Quadrant 2 and other digital modeling capabilities.
Fact sheet
Quadrant 2™: Predictive Platform for Solubility and Bioavailability Enhancement
White paper
Advancing drug development using in silico modeling
Fact sheet
Engineered solutions for oral solid dose product development
Determining product shelf life is a regulatory requirement for pharmaceuticals and an important consideration for packaging decisions.
Predictive accelerated stability studies allow the long-term stability characteristics of a drug substance or drug product to be characterized from extrapolation of results of short-term studies that measure, track, and quantify stability-indicating attributes, such as degradation, thermal properties, crystallinity, color, viscosity, and particle size.
Computational methods for accelerated stability assessment program (ASAP) studies are powerful tools for quickly and accurately predicting product shelf life and packaging options for tablets, capsules, softgels, intermediates, granules, blends, solutions, and suspensions.
Predictive stability modeling is widely accepted globally for early clinical trials (INDs/IMPDs. The data are also used in new drug approval (NDA) applications for the following purposes:
- Demonstrating the validity of models against ICH data
- Bridging clinical-to-commercial changes
- Justifying specification limit, formulation, or process changes
- Selecting commercial packaging
- Defining critical quality attributes
Post-approval applications include justification for reduced-protection packaging and acceptance of after-shipping excursions.
A quality by design (QbD) approach to developing drug dosage forms requires careful characterization and understanding of the properties and limitations of the product and process.
In silico process modeling offers advanced technologies such as compaction simulation, discrete element modeling (DEM), and computational fluid dynamics (CFD) for a wide range of applications, from material characterization and formulation development to process scale-up and tech transfer.
Compaction simulation techniques can be employed to evaluate the compaction behavior of materials in an accelerated and material-sparing way. Compaction simulators are computer-controlled devices programmed to precisely mimic the compression kinetics of any roller compactor or rotary tablet press equipment in real time. It enables evaluation of processes under identical manufacturing conditions.
To minimize manufacturing risks, compaction simulation studies are employed to achieve several key objectives:
- Assessment of potential compaction risks, such as capping, crack formation, high ejection forces, and speed sensitivity
- Strategy development for compaction speeds, compaction forces, and tablet hardness ranges
- Evaluation of punch sticking or picking risks
- Development of tablet formulations for desired dosage strengths
- Development of a dry granulation process
- Scale-up strategy planning and development
Discrete element modeling (DEM) is another powerful tool for understanding the behavior of powder during processing and for designing scale-up strategies. By providing a mechanistic understanding of particle dynamics in powder systems, DEM, coupled with computational fluid dynamics (CFD), offers critical insight for such pharmaceutical unit operations as pan coating, spray drying, fluid bed processing, and continuous manufacturing.
Rational drug discovery requires the early evaluation of various factors that influence a drug candidate's potential success through preclinical, clinical, and commercial development stages. Digital models specializing in ADME-PK (Absorption, Distribution, Metabolism, and Excretion - Pharmacokinetics) have emerged as crucial tools in drug development. These models offer significant benefits by improving decision-making and optimizing the development process. These computational models enable the prediction and simulation of a drug candidate's pharmacokinetic behavior, providing critical insights early in the development process.
Some of the key applications of ADME-PK modeling include:
- Dose bioavailability
- Sensitivity analysis
- Guidance on formulation design
- Mechanistic in vitro/in vivo correlations
- Understanding food effects
- Physiologically based PK modeling of preclinical and clinical data
- Predicting animal and first-in-human doses
- Assessment of drug–drug interactions
Digital modeling powered by AI, machine learning, and other innovative technologies, are transforming every stage of drug development by enhancing efficiency and precision, minimizing risks, and accelerating progress.
Frequently asked questions on predictive modeling
Predictive modeling uses computational tools to simulate drug behavior, formulation performance, and biological interactions, enabling faster and more informed development decisions.
PBPK modeling predicts how a drug is absorbed, distributed, metabolized, and excreted, helping guide dose selection and clinical study design.
It is most effective early in development but can support decision-making throughout formulation, clinical development, and regulatory submission stages.
Yes, by predicting outcomes before experimentation, it can reduce formulation iterations, conserve API, and accelerate development timelines.
OSDPredict™ integrates AI-driven modeling with CDMO expertise, linking predictions directly to formulation and manufacturing decisions.
Helpful Resources
White paper
Advancing drug development using in silico modeling
This report provides a framework for that understanding by outlining some of the processes that stand to gain the most from computational modeling and identifying the in silico capabilities that can be used to accelerate and de-risk each phase of development.
Whitepaper
How broadening the analysis of compound factors allows for predictive solubility solutions
The Biopharmaceutics Classification System (BCS), developed by the U.S. Food and Drug Administration to simplify and accelerate the drug development process, helps companies when they file for bioequivalence of dosage forms based on in vitro dissolution testing.
Blog post
CDMO 2.0: Three pharma industry trends for 2024 and beyond
Discover three major trends expected in the pharma industry, including turning to flexible manufacturing, embracing digital enablement, and the need for CDMOs deliver transformational value.
Blog post
In Silico Modeling: Accelerating drug development
In silico modeling, both in early development and across the product lifecycle, can streamline drug development and reduce the risks associated with trial-and-error experimental methods. Realizing the potential of the technology requires careful selection and application of in silico strategies and a deep understanding of how to interpret and derive the most valuable insights from the data.
Fact sheet
Quadrant 2™: Predictive Platform for Solubility and Bioavailability Enhancement
Download this fact sheet to learn more about the Quadrant 2™ predictive platform, an artificial intelligence- and machine learning–powered tool to enhance early drug development.