Comprehensive Analysis of Medication Discovery and Developments

Daniel Harris^*
*Correspondence: Daniel Harris, Department of Biomedical Engineering, Duke University, Durham, NC, USA, Email:

Description

Pharmaceutical businesses primarily attempt to provide new pharmaceuticals to the market through the complicated operations of drug research and development. New drugs are constantly required by healthcare systems to address unmet medical requirements across varied therapeutic areas. Target selection and validation, hit identification, lead creation and optimization, and finally the identification of a candidate for further development are all part of the discovery process. Development, on the other hand, comprises chemical synthesis and formulation optimization, animal toxicity research, clinical trials, and finally regulatory approval. Both of these procedures are time-consuming and costly, and the sector is now under pressure due to tight regulatory requirements, environmental concerns, and lower revenues due to patent expirations. These difficulties have had a negative impact on R&D productivity in recent years, necessitating new methodologies as well as increasing collaboration between business, academia, and government research institutes, all with the goal of producing high-quality medications on a consistent basis. This chapter will examine the preclinical discovery stage in depth, as well as the development methods. It will also discuss the challenges that the pharmaceutical sector faces, as well as novel initiatives that have the potential to safeguard the industry's long-term viability.

The discipline of pharmaceutical sciences known as drug metabolism and pharmacokinetics (DMPK) is very significant. In recent years, the nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has shifted from being primarily descriptive to a more quantitative and mechanistic understanding of drug candidates' fate in biological systems. In the last decade, tremendous progress has been made not only in the identification of design principles that can minimize Drug-Drug Interaction (DDI) potentials and reduce attritions, but also in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity. Every new technology reaches a stage in its development where concerns about its use and impact must be posed. Despite the fact that microfluidic technologies have dramatically reduced the scales at which laboratory procedures may be done and have enabled scientific advancements that would not have been feasible otherwise, it is time to explore if these technologies are more disruptive than enabling. My goals are to introduce researchers in the broad fields of drug discovery and development to the benefits and drawbacks of microfluidic technologies, to highlight current work demonstrating how microfluidic technologies can be used at various stages in the drug discovery and development process, and to discuss how academic breakthroughs in the field of microfluidic technologies can be transferred to industrial environments [1-5].

Artificial Intelligence (AI) has just lately begun to find its way into numerous areas of society, with the pharmaceutical business being one of the first to benefit. This review focuses on the impactful use of AI in various areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, increasing pharmaceutical productivity, clinical trials, and so on, to name a few, reducing human workload and achieving targets in a short amount of time. Crosstalk on AI enforcement tools and tactics, current issues and solutions, as well as the future of AI in the pharmaceutical business, is also covered.

Conflict of Interest

None.

References

1. Goyal, Himanshu, and Perrine Pepiot. “A compact kinetic model for biomass pyrolysis at gasification conditions.” Energy Fuels 31 (2017): 12120-12132.

Google Scholar, Crossref

2. Biernacki, Piotr, Sven Steinigeweg, Axel Borchert, and Frank Uhlenhu. “Application of anaerobic digestion model No. 1 for describing anaerobic digestion of grass, maize, green weed silage, and industrial glycerine.”  Bioresour Technol 127 (2013):188-194.

Google Scholar, Crossref, Indexed at 

3. Hodge, David B., M. Nazmul Karim, Daniel J. Schell, and James D. McMillan. “Model-based fed-batch for high-solids enzymatic cellulose hydrolysis.” Appl Biochem Biotechnol 152 (2008): 88-107.

Google Scholar, Crossref, Indexed at

4. Isikgor, Furkan H. and C. Remzi Becer. “Lignocellulosic biomass: A sustainable platform for production of bio-based chemicals and polymers.” Polym Chem 6 (2015): 4497-4559.

Google Scholar, Crossref

5. Fava, Fabio, Grazia Totaro, Ludo Diels and Maria Reis, et al. “Biowaste biorefinery in Europe: Opportunities and research & development needs.” New Biotechnol 32 (2015): 100-108.  

Google Scholar, Crossref, Indexed at