Ideality in Context: Motivations for Total Synthesis.

Total synthesis-the ultimate proving ground for the invention and field-testing of new methods, exploration of disruptive strategies, final structure confirmation, and empowerment of medicinal chemistry on natural products-is one of the oldest and most enduring subfields of organic chemistry. In the early days of this field, its sole emphasis focused on debunking the concept of vitalism, that living organisms could create forms of matter accessible only to them. Emphasis then turned to the use of synthesis to degrade and reconstitute natural products to establish structure and answer questions about biosynthesis. It then evolved to not only an intricate science but also a celebrated form of art. As the field progressed, a more orderly and logical approach emerged that served to standardize the process. These developments even opened up the possibility of computer-aided design using retrosynthetic analysis. Finally, the elevation of this field to even higher levels of sophistication showed that it was feasible to synthesize any natural product, regardless of complexity, in a laboratory. During this remarkable evolution, as has been reviewed elsewhere, many of the principles and methods of organic synthesis were refined and galvanized. In the modern era, students and practitioners are still magnetically attracted to this field due to the excitement of the journey, the exhilaration of creation, and the opportunity to invent solutions to challenges that still persist. Contemporary total synthesis is less concerned with demonstrating a proof of concept or a feasible approach but rather aims for increased efficiency, scalability, and even "ideality." In general, the molecules of Nature are created biosynthetically with levels of practicality that are still unimaginable using the tools of modern synthesis. Thus, as the community strives to do more with less (i.e., innovation), total synthesis is now focused on a pursuit for simplicity rather than a competition for maximal complexity. In doing so, the practitioner must devise outside-the-box strategies supplemented with forgotten or newly invented methods to reduce step count and increase the overall economy of the approach. The downstream applications of this pursuit not only empower students who often go on to apply these skills in the private sector but also lead to new discoveries that can impact numerous disciplines of societal importance. This account traces some select case studies from our laboratory over the past five years that vividly demonstrate our own motivation for dedicating so much effort to this classic field. In aiming for simplicity, we focus on the elusive goal of achieving ideality, a term that, when taken in the proper context, can serve as a guiding light to point the way to furthering progress in organic synthesis.

Total Synthesis of Tagetitoxin.

The intriguing structure of tagetitoxin (1), a long-standing challenge in natural product synthesis, has been the subject of multiple revisions and has been confirmed through total synthesis. The route commences from a renewable furan starting material and features a number of unusual transformations (such as rearrangements, bromocyclization, and P(V)-based phosphate installation) to arrive at the target in 15 steps. As the route was designed to enable access to both enantiomers, the absolute configuration of the natural product could be assigned using a bioassay on (+)-1 and (-)-1.

Antitubercular Triazines: Optimization and Intrabacterial Metabolism.

The triazine antitubercular JSF-2019 was of interest due to its in vitro efficacy and the nitro group shared with the clinically relevant delamanid and pretomanid. JSF-2019 undergoes activation requiring F420H2 and one or more nitroreductases in addition to Ddn. An intrabacterial drug metabolism (IBDM) platform was leveraged to demonstrate the system kinetics, evidencing formation of NO⋅ and a des-nitro metabolite. Structure-activity relationship studies focused on improving the solubility and mouse pharmacokinetic profile of JSF-2019 and culminated in JSF-2513, relying on the key introduction of a morpholine. Mechanistic studies with JSF-2019, JSF-2513, and other triazines stressed the significance of achieving potent in vitro efficacy via release of intrabacterial NO⋅ along with inhibition of InhA and, more generally, the FAS-II pathway. This study highlights the importance of probing IBDM and its potential to clarify mechanism of action, which in this case is a combination of NO⋅ release and InhA inhibition.

Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis.

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F420H2-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.

Concise Total Synthesis of Herqulines B and C.

A simple total synthesis of herqulines B and C is reported, modeled on the reductive biosynthesis reported previously by other researchers. Commencing from tyrosine, these alkaloids were fashioned through a dimerization, macrocyclization, and four consecutive reductions. Emerging from these studies are strategic insights on the synthesis of these strained alkaloids, as well as mild conditions for the exhaustive reduction of diketopiperizines.

Addressing the Metabolic Stability of Antituberculars through Machine Learning.

We present the first prospective application of our mouse liver microsomal (MLM) stability Bayesian model. CD117, an antitubercular thienopyrimidine tool compound that suffers from metabolic instability (MLM t1/2 < 1 min), was utilized to assess the predictive power of our new MLM stability model. The S-substituent was removed, a set of commercial reagents was utilized to construct a virtual library of 411 analogues, and our MLM stability model was applied to prioritize 13 analogues for synthesis and biological profiling. In MLM stability assays, all 13 analogues had superior metabolic stability to the parent compound, and six new analogues had acceptable MLM t1/2 values greater than or equal to 60 min. It is noteworthy that whole-cell efficacy and lack of relative mammalian cell cytotoxicity could not be predicted simultaneously. These results support the utility of our new MLM stability model in chemical tool and drug discovery optimization efforts.

Open drug discovery for the Zika virus.

The Zika virus (ZIKV) outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. METHODS: Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). RESULTS: "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. CONCLUSIONS: Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 µg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 µg/mL versus Mtb and a CC50 in Vero cells of >40 µg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 µM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.