microbiome and its connection to cardiovascular, immunologic, and neurologic “health” will likely expand target space and point to new approaches for therapeutic intervention. This is tied to the re‐emergence of phenotypic drug discovery in general and all of this could be accelerated via the application of present‐day genomics tools coupled with the state‐of‐the‐art informatics capabilities.
A group of my MSD Chemistry colleagues recently rejected the position that the act of designing and synthesizing molecules is a commoditized aspect of pharmaceutical research, but rather “that excellence and innovation in synthetic chemistry continues to be critical to success in all phases of drug discovery and development [32].” This sentiment echoes the recent views of other chemists from both academic and industrial circles [33–36] and I can point to multiple times over my 35 year career where organic synthesis has indeed been a significant inspiration of my ideas (both good ones and bad ones). Looking forward, the capability exists for most industrial medicinal chemists to rapidly work through the more empirical aspects of their drug discovery projects using powerful automation tools [37] enabled by machine learning and artificial intelligence and this will likely become part of the standard academic training regimen for the next generation of drug discovery and development chemists [38]. The horizons of organic synthesis continue to expand via the continued innovation in areas of chemo‐ and biocatalysis [39], electrochemistry [40], flow chemistry [41], methods to productively disconnect carbon–carbon bonds [42], and photochemically induced oxidation/reduction chemistry [43]. These will not only afford access to new biologically interesting chemical space for medicinal chemists, but also pave future paths to design concepts. Synthesis is not (nor should it be) the only driver of molecular design and it is presently complemented by current capabilities of machine learning to understand and optimize molecular interactions as well as finely balance the molecular properties critical to bioperformance (absorption, distribution, metabolism, excretion, and toxicity) [44]. While there are currently growing pains with machine learning in drug research [45], it has the potential to positively impact future drug research efforts.
In the era when we see public figures staking claim to “alternative facts” and regularly hear claims that inconvenient or uncomfortable (but objectively verifiable) information is “fake news”, broad societal pressures and the “human element” may pose the biggest threat to continued new discoveries in drug research. In his ground‐breaking 2005 book The World Is Flat: A Brief History of the Twenty‐first Century, Thomas Friedman lays out how the digitization of information and the broad reach of the internet has led to the rapid democratization of capabilities and knowledge [46]. Anyone connected to or embedded in drug research now regularly grapples with the resulting positive and negative impacts of Friedman's flat world. While we can marvel at the power and convenience of conducting broad literature searches in seconds by simply clicking a mouse, this comes at a cost to researchers of having to discern increasingly faint signals in an exponentially expanding sea of noise. As we enhance our ability to leverage artificial intelligence [47] and exploit big data we may be able to eventually corral large quantities of disparate sets of information that is relevant to drug discovery and development, but pitfalls that can be traced to the humans behind the computers have already come to the fore [48].
I also believe that there are significant risks to future innovations posed by the fracturing of the complex research environments into discrete units and continued efforts to optimize their individual parts. This has manifested itself in a pharmaceutical “gig economy” that can reinforce a short‐term, transactional mindset and poses significant challenges to individuals who may be more accustomed to or who would thrive in more traditional research environments [49]. In order to ensure we are on the path to discovering future medicines, we must realize that the rigorous application of the scientific method coupled with savvy decision‐making is critical. The disciplined fostering of individual and group behaviors that promote innovation should be a priority. Making sure that we tolerate failure, allow for experimentation, ensure psychological safety, promote collaboration and demand that we have leaders who support all of this is crucial to have if we seek to avoid having promising research undercut by non‐productive human intervention [50].
While we in the field of medicinal chemistry and drug research and development find ourselves in an era of great challenges, I am optimistic about the future of the discipline. The chapters in this volume that detail efforts that resulted in new drugs to treat bacterial and viral infections, numerous metabolic disorders and various cancers serves as a testament to the creativity, persistence, technical skill and innovative insights of the many individuals and large teams required to make these therapeutics a reality. Beyond that, none of this would have been possible if not for the dedication and effort of those researchers who happen to have direct connections to the specific biologic targets and bioactive agents described herein. The contributions of many scientists to the disciplines of biochemistry, molecular biology, pharmacology, toxicology, and translational sciences are all necessary to advance biomedical research and is the foundation upon which all of the case studies stand. These previous words of Malcolm MacCoss still ring true to me – “… the noble endeavor of drug discovery must continue to move forward, even if the path is steep and the costs continue to rise. We must persevere because otherwise our children and their children will be restricted to using only the drugs of their parents to fight their battles with the same devastating diseases, despite all the wonderful discoveries in medicine and the biological sciences …” I hope that the readers of this volume are inspired by the case studies described and conclude as I did that despite the great challenges in front of us in combatting human disease, the future provides us with the great opportunity to impact the course of human health.
References
1 1 World Health Organization (2018). The top ten causes of death. https://www.who.int/news‐room/fact‐sheets/detail/the‐top‐10‐causes‐of‐death (accessed 29 September 2021).
2 2 Bloom, D.E. and Cadarette, D. (2019). Infectious disease threats in the twenty‐first century: strengthening the global response. Front. Immunol. 10: 1–12.
3 3 Trypsteen, W., Van Cleemput, J., van Snippenberg, J. et al. (2020). On the whereabouts of SARS‐CoV‐2 in the human body: a systematic review. PLoS Pathog.: 1–26.
4 4 U.S. Department of Health and Human Services(2017). United States life tables, 2017. https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07‐508.pdf (accessed 29 September 2021).
5 5 Vogenberg, F.R. (2019). U.S. Healthcare trends and contradictions in 2019. Am. Health Drug Benefits 12 (1): 40–47.
6 6 Hay, M., Thomas, D.W., Craighead, J.L. et al. (2014). Clinical development success rates for investigational drugs. Nat. Biotechnol. 32 (1): 40–51.
7 7 Smietana, K., Siatkowski, M., and Mølle, M. (2016). Trends in clinical success rates. Nat. Rev. Drug Discov. 15: 379–380.
8 8 Thomas, D.W., Burns, J., Audette, J. et al. (2017). Clinical development success rates 2006–2015. https://www.bio.org/sites/default/files/legacy/bioorg/docs/Clinical%20Development%20Success%20Rates%202006‐2015%20‐%20BIO,%20Biomedtracker,%20Amplion%202016.pdf (accessed 29 September 2021).
9 9 Wong, C.H., Siah, W.W., and Lo, A.W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics 20 (2): 273–286.
10 10 Bansal, R., De Backer, R., and Ranade, V. (2018). What's behind the pharmaceutical sector's M&A push. https://www.mckinsey.com/business‐functions/strategy‐and‐corporate‐finance/our‐insights/whats‐behind‐the‐pharmaceutical‐sectors‐m‐and‐a‐push# (accessed 29 September 2021).
11 11 Patterson,