In-silico modeling services

Enabling smarter decision making, accelerated R&D, and risk mitigation

In silico modeling technologies 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's array of advanced computational and digital modeling capabilities, referred to collectively as our Engineered Solutions support a comprehensive approach to drug product development by enabling smarter decisions based on a better understanding of different product parameters.

Comprising proprietary and industry-standard technologies, these solutions are instrumental in de-risking and speeding up each phase of drug development. Specific applications include:


Advantages of Thermo Fisher Scientific’s Engineered Solutions:

  • Mitigate development risks and minimize experimental failures
  • Expedite development timelines
  • Reduce overall costs
  • Conserve valuable API resources
  • Enable informed decision-making and rational drug design
  • Diminish dependency on animal testing

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. 

In silico modeling services, solutions, and capabilities

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. Thermo Fisher Scientific’s proprietary platform for solubility and bioavailability enhancement, Quadrant 2, is a diagnostic tool for early development, which allows us to see in-silico predictions of formulations, which can save time and costs compared with trial-and-error approaches

Quadrant 2

Learn about our proprietary platform for solubility and bioavailability enhancement, Quadrant 2TM here.

Quadrant 2 - Thermo Fisher Scientific's AI and machine learning enabled predictive modeling for solubility and bioavailability enhancement

Helpful resources 

Browse our resource library to learn more about Quadrant 2 and other digital modeling capabilties.


Quadrant 2 predictive platform for solubility and bioavailability enhancement 

Fact Sheet

Engineered Solutions for oral solid dose product development

White Paper

Advancing drug development using in silico modeling


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
Accelerated stability modeling for shelf life and packaging determination

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
Compaction simulation and process modeling

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
ADMI-PK (PBPK) Modeling

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