Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review

Abstract

The drug regularization process involves many steps that are complex and time-consuming. The demand for new drugs has prompted researchers and regulatory authorities to search for predictive methods that can streamline the development process. Current studies point to innovative computational techniques in a drug's study phases. This study aims to carry out a scoping review of research involving the application of computational methods and in silico studies in the clinical research and development of new drugs. A scoping review was conducted according to the eferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Online databases from 2001 to 2021 in English were used and the trial registration was 10.17605/OSF.IO/USXCM. The development of protocols and the application of a computational method for researching new drugs and their formulation, published in a peer-reviewed journal, were included. The data extraction and analysis were performed by two independent reviewers. In this study, 312 articles were retrieved, of which 6 were duplicates. After the title was read, only 101 remained for analysis. After the abstracts were read, 34 papers were considered for the scoping review. The use of in silico methodologies has been expanding in terms of research into the development of new drugs and the improvement of existing products.

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

“…Computational toxicology refers to the use of computer and information technology tools to collect, analyze, and model chemical toxicity data (Hartung and Hoffmann, 2009;Hemmerich and Ecker, 2020;Ferreira Matos et al, 2022). It represents a central pillar of 21 st century green toxicology given its potential to dramatically increase human relevance, efficiency, and predictive capacity while reducing animal testing needs (Hartung, 2017).…”