China
Africa
Explore chapters and articles related to this topic
Phytonanotechnology
Published in Namrita Lall, Medicinal Plants for Cosmetics, Health and Diseases, 2022
Tafadzwa J. Chiome, Asha Srinivasan
Drug discovery is a process by which potential and novel plant-based compounds are discovered after screening for therapeutic efficacy. The process through which a lead compound can be transformed into a commercial product can take up to 10–15 years for its development. Pharmaceutical companies have several approaches toward new drug discovery and development. However, two approaches are favored: phenotypic drug discovery and target-based drug discovery. In both these methods, the new drug discovery process involves rational drug design after initial target identification. In target-based drug discovery, the target is generally a gene or a protein that is linked with a disease. The success of new drug design is dependent on a target identification of a particular drug. The ability of a drug to bind to its target is assessed, with a thorough understanding of structural and thermodynamic basis on protein-ligand interactions, as it is this interaction that will initiate a cascade of biological events in response to the drug (Baker et al., 1995).
Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
A rigorous search for potential drugs to treat and prevent SARS-CoV-2 infection was conducted by many researchers utilizing both computational methods and experimental techniques, and enormous efforts have been made to identify potent drugs for COVID-19 based on drug repurposing and finding potential novel compounds from ligand libraries, natural products, short peptides, and RNAseq analysis [59]. In general, various entities with different levels of structural and organizational complexity can serve as drugs [60]. These entities could be small chemical compounds and (stapled) peptides [61–69], various therapeutic proteins such as antibodies or nanobodies [70–81], vaccines [82, 83], and even entire cells [84–86]. Computer-aided drug discovery represents an important means of enabling cost- and time-efficient development of new drugs and target-specific drugs to combat any disease, including COVID-19. Computational approaches in drug discovery are traditionally focused on finding targets for drug design and on identifying lead compounds. In application to viral infections, the search for drug design targets can be further subdivided into identification of viral and host targets. The sections below briefly introduce some of the computational methods used in these endeavors. It is worth noting, however, that most of the computer-aided drug discovery tools require knowledge of the structure of various target proteins present in SARS-CoV-2, or the structures of target host proteins.
Biogenic Nanoparticles Based Drugs Derived from Medicinal Plants
Published in Richard L. K. Glover, Daniel Nyanganyura, Rofhiwa Bridget Mulaudzi, Maluta Steven Mufamadi, Green Synthesis in Nanomedicine and Human Health, 2021
Charles Oluwaseun Adetunji, Olugbenga Samuel Michael, Wilson Nwankwo, Osikemekha Anthony Anani, Juliana Bunmi Adetunji, Akinola Samson Olayinka, Muhammad Akram
Bioinformatics plays a major role in rational drug design, thus reducing the time and cost of drug development. The foregoing is true in the synthesis of drugs as bioinformatics provides comprehensive analytical tools and huge databases for modelling of proteins, genes and related biological compounds as well as the evaluation of the interaction and behaviour, including compatibility and functionalities. In other words, it provides a very cost-effective in silico environment for simulating, verifying and validating the behaviour of drug and drug-related compounds in biological systems.
The discovery of novel antivirals for the treatment of mpox: is drug repurposing the answer?
Published in Expert Opinion on Drug Discovery, 2023
Ahmed A. Ezat, Jameel M. Abduljalil, Ahmed M. Elghareib, Ahmed Samir, Abdo A. Elfiky
Other supportive in silico techniques have also greatly boosted drug and vaccine discovery and enabled fast drug screening with near experimental accuracy. The emergence of artificial intelligence (AI) and machine learning (ML) and their integration into drug design and discovery pipelines have further coerced drug discovery and development. Alpha-fold2 is one successful example of AI applications in the computational structural biology field. They are a type of data-driven research methodology that can be used in drug discovery. They mine a small number of annotated data sets to generate hypotheses about the structure and function of proteins [67]. The experiments produced in these studies often lead to the discovery of new drug candidates. Drug companies use these methods to identify potential drug targets quickly [89]. In recent years, alpha-fold methods have successfully identified new targets for treating various diseases. This approach reduces the research cost and increases drug development’s efficiency [90].
A patent review of selective CDK9 inhibitors in treating cancer
Published in Expert Opinion on Therapeutic Patents, 2023
Tizhi Wu, Xiaowei Wu, Yifan Xu, Rui Chen, Jubo Wang, Zhiyu Li, Jinlei Bian
Newly reported CDK9 inhibitors patented in recent years mainly include aminopyrimidine/pyridine/triazine derivatives, acylpyridine/pyrimidine derivatives, aminopyrimidine pyrazole/pyrrole derivatives, azaindoles or azabenzimidazoles derivatives. Except for compounds 62 and 63, most of them are analogs of the clinical candidates, including compound 2, 23 and 41, which generally share the same mechanism of action with CDK9. However, there is still a lack of safe and effective CDK9 inhibitors on the market. Hence, suitable drug design approaches are essential. Currently, computer-assisted drug design (CADD) is the primary mean for the discovery and optimization of most CDK9 inhibitors [14]. Still, the limitations of computer technology hinder the use of such strategies to step out of the template of previous studies [92]. Phenotypic screening, which directly evaluating the ability of compounds to intervene in disease-related phenotypes, is now returning to researchers’ attention and may markedly reduce the impact of these problems [93,94]. The author thinks that the combined use of phenotypic screening and CADD might greatly facilitate the discovery of novel and effective CDK9 inhibitors. On the other hand, searching for new binding modes between ligand and receptor (e.g. covalent binding) or new protein binding sites (e.g. allosteric site) is extremely meaningful, as it could greatly stimulate the discovery of novel CDK9 inhibitors and contribute to the further investigation of the biological functions of CDK9.
Reinforcement learning for systems pharmacology-oriented and personalized drug design
Published in Expert Opinion on Drug Discovery, 2022
Ryan K. Tan, Yang Liu, Lei Xie
Generating molecules in silicon is far from the final goal of developing drugs. The first step after molecular design is to synthesize designed compounds and test their effects with wet experiments. However, not all computational designed molecules are synthesizable. This seriously affects the practical value of computational drug design. Some studies try to resolve this by taking synthesizability into consideration while generating molecules. Reaction-driven objective reinforcement (REACTOR) empowered by actor-critic method, for example, defines the state-action trajectory in RL as a sequence of chemical reactions, and thus not only improves the synthesizability of the generated molecules, but also speeds up the exploration rate of the model in the chemical space [84]. Additionally, REACTOR employs a synchronous version of A3C which can perform parallelized policy search and thus tremendously improves the efficiency of the policy training. In addition to REACTOR, there are other works that leverage actor-critic methods for synthesis-oriented molecule generation, such as Towered Actor-Critic (TAC) [85] and Policy Gradient for Forward Synthesis (PGFS) [86].