Despite many advances in the last decades, the process of drug discovery is still very time-consuming, risky and costly with a typical drug discovery and development cycle taking approximately 14 years to reach the market. Bioinformatics and computational approaches (generally called in silico approaches) offer promising avenues to design new molecules in silico, systematically assess potential lead candidates, study chemical interactions and perform general pharmacokinetics and pharmacodynamics. Lately, also other in silico tools are increasingly used in the drug development context, capturing the physiological processes, the disease as well as the drug action. This allows answering different questions, make long-term predictions and perform in silico clinical trials. A prerequisite for the use of in silico tools in drug development is the existence of appropriate guidelines for reporting to and assessment by regulatory bodies. Said guidelines exist for classical pharmacokinetics and pharmacodynamics but are still in development for other in silico model technologies.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of bioinformatics and computational approaches for the development of innovative genetic and cellular therapies, covering all in silico technologies and phases of the therapy development life cycle. In particular, we would like to bring together contributions where bioinformatics and computational approaches have contributed to development of gene-, peptide- and RNA-based therapeutics as well as to advanced cell based therapies. Contributions can take the form of original research, reviews and perspectives papers.
Areas to be covered in this Research Topic may include, but are not limited to:
* Development of new machine learning techniques for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Advanced bioinformatics approaches for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Development of approaches for in silico clinical trials
* white-box / mechanistic models for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Validation, verification and uncertainty quantification
* Regulatory science related to in silico models of drug development
Despite many advances in the last decades, the process of drug discovery is still very time-consuming, risky and costly with a typical drug discovery and development cycle taking approximately 14 years to reach the market. Bioinformatics and computational approaches (generally called in silico approaches) offer promising avenues to design new molecules in silico, systematically assess potential lead candidates, study chemical interactions and perform general pharmacokinetics and pharmacodynamics. Lately, also other in silico tools are increasingly used in the drug development context, capturing the physiological processes, the disease as well as the drug action. This allows answering different questions, make long-term predictions and perform in silico clinical trials. A prerequisite for the use of in silico tools in drug development is the existence of appropriate guidelines for reporting to and assessment by regulatory bodies. Said guidelines exist for classical pharmacokinetics and pharmacodynamics but are still in development for other in silico model technologies.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of bioinformatics and computational approaches for the development of innovative genetic and cellular therapies, covering all in silico technologies and phases of the therapy development life cycle. In particular, we would like to bring together contributions where bioinformatics and computational approaches have contributed to development of gene-, peptide- and RNA-based therapeutics as well as to advanced cell based therapies. Contributions can take the form of original research, reviews and perspectives papers.
Areas to be covered in this Research Topic may include, but are not limited to:
* Development of new machine learning techniques for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Advanced bioinformatics approaches for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Development of approaches for in silico clinical trials
* white-box / mechanistic models for drug discovery, gene-, peptide- and RNA-based therapeutics and advanced cell based therapies
* Validation, verification and uncertainty quantification
* Regulatory science related to in silico models of drug development
