InSilicoMinds - Leverage our In Silico Solutions for Large Molecules Drug Development

Large Molecules

Modeling and simulation techniques have revolutionized the field of drug discovery and development, enabling researchers to gain a deeper understanding of the molecular mechanisms underlying disease and the action of potential therapeutics. In particular, these techniques have proven to be invaluable in the development of large molecules such as biologics, proteins, and peptides. By allowing researchers to visualize and manipulate the structure and function of these complex molecules, modeling and simulation techniques can help to optimize their performance and improve their safety and efficacy.

We have several different types of in silico techniques that can be used for large molecules, including molecular dynamics simulations, quantum mechanics calculations, and homology modeling. Each of these techniques has its own strengths and limitations, and the choice of technique will depend on the specific research question being addressed.

Model Informed Generic Drug Development

Our AI, Modeling & Simulation experts work closely with our clients to understand their unique scientific questions of interest and challenges. We then leverage our expertise in in silico modeling and simulation to provide the correct approach to address our customer requests.

Pharmaceutical Digitization

We have extensive expertise in modeling and simulation for a wide range of applications, including pharmaceuticals, API, and process simulations. We are skilled in developing custom models and simulations to meet specific client needs. Our team is committed to staying up-to-date with the latest developments in the field and applying cutting-edge techniques to drive innovation and optimization.

We are skilled in developing custom validated models

Drug repurposing

Drug repurposing refers to the process of discovering new therapeutic uses for existing drugs that were originally developed for a different indication. Our in silico solution predicts putative biological targets and corresponding bioactivity of a query compound based on a large database covering a chemical space of more than 600000 molecules and 1000000 activity records. This approach can potentially save time and reduce costs in drug development since the drug's safety profile and pharmacokinetics are already known. Additionally, it can help to address unmet medical needs, especially for rare or neglected diseases.

Bioactivity predictions

Our in silico solution, for a given small molecule chemical structure, enables quantitative bioactivity predictions (Ki, EC50, IC50, Kd) towards the identified putative protein targets and the corresponding estimate precisions.

Molecular Docking

Molecular docking is a computational technique used to predict the binding mode and strength between two molecules, typically a protein and a ligand. Our in silico solution explores the interactions between a ligand and a biological target at a molecular level, evaluates potential target-ligand combinations, and quantifies target-ligand binding affinities.

Protein-Protein Interactions

Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Our in silico solution predicts protein-protein interaction probability through 2D maps.

Epitome identification

Epitome consists of all known antigen/antibody complex structures, a detailed description of the residues that are involved in the interactions, and their sequence chemical/structure environments. Our in silico solution predicts protein-protein interactions probability through 2D maps.

Target Identification

Our in silico solution identifies, for a given large molecule chemical structure, the putative drug target finishing via reverse ligand-based screening. Our tool is built on the similarity principle and on a collection of > 600,000 compounds known to be experimentally active on > 6,000 protein targets from ChEMBL. Apart from the identified targets, the tool also provides information on the most similar bioactive ligands to the large molecule along with the best activity experimental values.

Impurities and Toxicological Endpoints

Our in silico solution evaluates a compound’s toxicological prolife by in silico assessment of the major toxicological endpoints (e.g. genotoxicity, neurotoxicity, carcinogenicity, skin irritation, etc.).

QSAR

Our QSAR in silico solutions help predict the properties of a chemical compound based on its structure which allows us to quickly screen a large number of compounds and identify the ones with the most promising properties for further study.

PhysChem Properties

Our in silico solution quantifies Blood-Brain-Barrier (BBB) permeability to predict whether a compound will cross the BBB. It also predicts a whole range of molecular PhysChem properties.

ADME

Our in silico tool predicts ADME properties of a compound. (absorption, distribution, metabolism and elimination)

PK modeling

Our Pharmacokinetic (PK) modeling solutions help drug developers understand a drug's effects on the body by analyzing its absorption, distribution, metabolism, and excretion (ADME) properties.

Efficacy and Safety

Our modeling and simulation solutions can be used to predict the efficacy and safety of drugs. Our approach involves using validated computational models to simulate the behavior of a drug in the body and predict its efficacy and safety.

Biomarkers

Biomarkers are targeted to improve the diagnosis, prognosis, and therapeutics. Our in silico solution identifies ideal biomarkers which translate to personalized medicine and overall enhanced clinical outcomes.

IVIVE

IVIVE models are used to predict the in vivo performance of a drug based on its in vitro characteristics. Our in silico solutions help optimize drug formulations and predict the effect of formulation & process changes on the drug's performance.

BA BE studies

Our in silico modeling and simulation solutions streamline BA BE studies by offering cost savings, reduced time, reduced variability, and overall improved drug product design.

Allometric Scaling

Our validated Allometric Models are used to predict how a drug or chemical will be distributed, metabolized, and eliminated in the bodies of different species, including humans. Our inter-species predictions allow us to understand complex interactions between drugs and the body. We reduce the need for animal testing, increase speed, efficiency, and safety, and improve accuracy.

Virtual Patient Population

Our in silico solutions use mathematical models to simulate the behavior of a virtual population of patients. This approach can be used to predict the efficacy and safety of new drugs, optimize dosing strategies, and identify patient subgroups that are most likely to benefit from a particular treatment.

Trial Design Optimization

Our in silico solutions allow researchers to test different trial designs virtually, identify potential issues or limitations, and make changes before conducting the actual trial. Save time and resources, reduce the risk of failure, and ultimately improve the chances of success for your clinical trials.

Disease Progression

Our validated disease models help simulate the progression of a disease and predict its behavior in response to different treatments. Our models help researchers identify new drug targets, optimize treatment strategies, and accelerate drug development.

IO Combination

Immuno-Oncology therapy (or I-O therapy) is an emerging pillar of cancer treatment that utilizes the body's own immune system to fight diseases. Our modeling and simulation approaches are used to test a wide range of IO combinations, which can help to identify new and innovative solutions without being limited by physical testing alone.

Analytical-Based Meta-Analysis

Meta-analysis refers to the statistical analysis of the data from independent primary studies focused on the same question, which aims to generate a quantitative estimate of the studied phenomenon, for example, the effectiveness of the intervention. Our in silico solutions for Meta-Analysis combine the results of multiple experiments and integrate the test results through machine learning algorithms to deal with data complexity and heterogeneity.

Value of in silico modeling and simulation
in Large Molecule Generics.

Optimize Drug Formulations

Identify the optimal formulation and dosage forms that leads to improved drug efficacy and patient outcomes

Reduce Cost and Time to Market

Reduce the time and costs associated with clinical trials and drug development

Improve Regulatory Submissions

Help in streamlining the approval process and reduce the risk of regulatory rejection

De-risk
R&D

In silico allows informed decision-making early, de-risking R&D

Enhance Product Lifecycle Management

Identifying opportunities for reformulation or repurposing

Large Molecules

Model Informed Generic Drug Development​