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Target selection and validation involves identifying and validating a biological target that is involved in the disease process and that can be modulated by a drug to produce a therapeutic benefit. By carefully selecting and validating a target, drug developers can increase the chances of success in developing a new drug. Once a target has been validated, it can be used to design and develop drugs that interact with that target and modulate its function.

Significance of target selection and validation

The success of any drug majorly depends on its ability to aim for “the right target” which increases the drug’s efficacy. This is possible when researchers have an adequate understanding of disease biology. Insufficient knowledge or failure at this stage would result in poor target choice, and incorrect biomarkers identification and can hamper patient selection during the trials. This leads to significant loss of time, delays in drug development, and financial losses as well.

Therefore, target selection and validation are critical steps in the drug development process, and they play a vital role in determining the success or failure of a drug development program. The process involves identifying a molecular target or pathway that is involved in a particular disease and then testing the target’s suitability for drug development. Target selection involves identifying a specific biological target, such as a protein or enzyme, that is involved in a disease. The target must be essential for the disease process and have a known mechanism of action. This requires an understanding of the disease biology and the specific molecular pathways that are involved in the disease.

The Role of Quantitative Systems Pharmacology (QSP)

Target identification and validation is an area where Quantitative Systems Pharmacology (QSP) can play a crucial role in strengthening the drug discovery and development process. QSP is a modeling approach that integrates quantitative information about drug properties, pharmacokinetics, and pharmacodynamics to develop mechanistic models of drug action in biological systems.

One of the key benefits of QSP is its ability to incorporate data from multiple sources, including preclinical studies, clinical trials, and real-world data, to create more accurate and comprehensive models of drug action. By simulating the behavior of a drug in the body, QSP models can identify potential targets, predict the effects of different doses and regimens, and optimize the design of clinical trials. QSP can also help to improve target validation by providing a more detailed understanding of the underlying biological mechanisms involved in disease and drug action. By simulating the complex interactions between drugs, cells, and tissues, QSP models can identify the most promising targets for drug development during the In-silico testing and help to prioritize them for further investigation.

Use of QSP Models

QSP models can be used to simulate the complex interactions between drugs and their targets, as well as the effects of drugs on physiological systems. These models can provide insights into the mechanisms of action of drugs and help identify the most promising drug targets based on their potential to modulate disease-relevant pathways.

Furthermore, QSP can be used to optimize the selection of preclinical models, which can help ensure that the efficacy and safety of drugs are evaluated in the most relevant experimental systems. QSP models can be used to predict the pharmacokinetics and pharmacodynamics of drugs in different animal species and humans, which can inform decisions about the appropriate preclinical models for drug development.

For example, QSP models have been used to validate the target of the drug Gleevec for the treatment of chronic myeloid leukemia. These models showed that Gleevec was effective in blocking the activity of the target protein, which led to the approval of Gleevec for the treatment of chronic myeloid leukemia.

QSP in clinical trial design

QSP can also be used to support clinical trial design by predicting the effects of different dosing regimens and patient populations on drug efficacy and safety. QSP models can also be used to identify biomarkers that can be used to monitor drug response in clinical trials, which can help accelerate drug development by providing early evidence of efficacy and safety.

Overall, QSP represents a powerful tool for improving the success rate of drug discovery and development by enhancing target selection and validation. By leveraging the power of computational modeling, QSP can help researchers to identify the most promising drug candidates, optimize their properties, and accelerate the development process. It can help in providing a quantitative framework for evaluating drug targets and predicting their effects in complex biological systems. QSP can improve target selection and validation by providing a quantitative framework for integrating biological and pharmacological knowledge, predicting the effects of drugs on complex biological systems, and informing decisions about preclinical and clinical trial design.

To summarize the role of QSP in Target selection and validation, QSP can:

  • Help to identify potential drug targets and provide a better understanding of the biological processes involved.
  • Prioritize targets based on potential efficacy by comparing different targets.
  • Predict the effects of different dosing regimens and optimize the design of clinical trials.
  • Facilitate early and more thorough In-silico testing of drug candidates.
  • Provide insights on the potential pharmacodynamic and pharmacokinetic effects of the drug.
  • Identify biomarkers to monitor drug response during clinical trials.
  • Support clinical trial design and predict the effects of dosing regimens on patient populations on drug safety and efficacy.

Why choose Excelra?

At Excelra, we have a robust and refined approach to collecting, aggregating, and processing routine pharmacological data. Our expertise spans the entire drug discovery and development process, from target identification and validation to clinical trials and post-market monitoring. Many of the world’s largest pharma companies rely on our scientific products and data solutions to enhance and accelerate their decision-making process. If you are looking to accelerate drug discovery and development, we can help you achieve your goals.

Why Excelra:

  • We support Quantitative Systems Pharmacology teams directly, streamlining their work. We provide custom curation services, delivering indication-specific clinical, preclinical, and in vitro biomarker data for disease modeling.
  • We provide custom curation for predicting exposure-response (efficacy/safety), target identification and validation, and biomarker identification as well.
  • Our data includes extensive indication-specific and drug-specific pharmacokinetic (PK), pharmacodynamic (PD), and clinical endpoint data.
  • Our clinical data sets include pharmacology, toxicology, and biomarker data.
  • Our data is standardized, organized, and formatted into high-quality, analysis-ready data sets to ensure minimal friction during the analysis process.
  • Our dedicated pharmacologists deliver highly structured analysis-ready data sets faster and cheaper than in-house teams.
  • We’ve also delivered clinical trial outcomes data sets (CTOD) for over 130 indications across a wide range of therapeutic areas. Our team has processed data from more than 30,000 clinical trials.
  • We offer preclinical toxicology report digitization (PTRD) services to all our clients, further enhancing their decision-making resources.

Speed your drug discovery process with expert support in target selection and validation using Quantitative Systems Pharmacology.