Although the significance of human genetic polymorphisms in therapeutic outcomes is well established, the importance of our “second genome” (the microbiome) has been largely overlooked. In this Review, we highlight recent studies that shed light on the mechanisms linking the human gut microbiome to the efficacy and toxicity of xenobiotics, including drugs, dietary compounds and environmental toxins. Continued progress in this area could enable more precise tools for predicting patient responses and the development of a next generation of therapeutics based on or targeted at the gut microbiome. Indeed, the admirable goal of precision medicine may require us to first understand the microbial pharmacists within.

The human gut microbiota metabolizes the Parkinson’s disease medication Levodopa (l-dopa), potentially reducing drug availability and causing side effects. However, the organisms, genes, and enzymes responsible for this activity in patients and their susceptibility to inhibition by host-targeted drugs are unknown. Here, we describe an interspecies pathway for gut bacteriall-dopa metabolism. Conversion ofl-dopa to dopamine by a pyridoxal phosphate-dependent tyrosine decarboxylase fromEnterococcus faecalisis followed by transformation of dopamine tom-tyramine by a molybdenum-dependent dehydroxylase fromEggerthella lenta. These enzymes predict drug metabolism in complex human gut microbiotas. Although a drug that targets host aromatic amino acid decarboxylase does not prevent gut microbiall-dopa decarboxylation, we identified a compound that inhibits this activity in Parkinson’s patient microbiotas and increasesl-dopa bioavailability in mice.

In most modern organic chemistry reports, including many of ours, reaction optimization schemes are typically presented to showcase how reaction conditions have been tailored to augment the reaction's yield and selectivity. In asymmetric catalysis, this often involves evaluation of catalyst, solvent, reagent, and, sometimes, substrate features. Such an article will then detail the process's scope, which mainly focuses on its successes and briefly outlines the "limitations". These limitations or poorer-performing substrates are occasionally the result of obvious, significant changes to structure (e.g., a Lewis basic group binds to a catalyst), but frequently, a satisfying explanation for inferior performance is not clear. This is one of several reasons such results are not often reported. These apparent outliers are also commonplace in the evaluation of catalyst structure, although most of this information is placed in the Supporting Information. These practices are unfortunate because results that appear at first glance to be peculiar or poor are considerably more interesting than ones that follow obvious or intuitive trends. In other words, all of the data from an optimization campaign contain relevant information about the reaction under study, and the "outliers" may be the most revealing. Realizing the power of outliers as an entry point to entirely new reaction development is not unusual. Nevertheless, the concept that no data should be wasted when considering the underlying phenomena controlling the observations of a given reaction is at the heart of the strategy we describe in this Account. The idea that one can concurrently optimize a reaction to expose the structural features that control its outcomes would represent a transformative addition to the arsenal of catalyst development and, ultimately, de novo design. Herein we outline the development of a recently initiated program in our lab that unites optimization with mechanistic interrogation by correlating reaction outputs (e.g., electrochemical potential or enantio-, site, or chemoselectivity) with structural descriptors of the molecules involved. The ever-evolving inspiration for this program is rooted in outliers of classical linear free energy relationships. These outliers encouraged us to ask questions about the parameters themselves, suggest potential interactions at the source of the observed effects, and, of particular applicability, identify more sophisticated physical organic descriptors. Throughout this program, we have integrated techniques from disparate fields, including synthetic methodology development, mechanistic investigations, statistics, computational chemistry, and data science. The implementation of many of these strategies is described, and the resulting tools are illustrated in a wide range of case studies, which include data sets with simultaneous and multifaceted changes to the reagent, substrate, and catalyst structures. This tactic constitutes a modern approach to physical organic chemistry wherein no data are wasted and mec...

Although asymmetric catalysis is universally dependent on spatial interactions to impart specific chirality on a given substrate, examination of steric effects in these catalytic systems remains empirical. Previous efforts by our group and others have seen correlation between steric parameters developed by Charton and simple substituents in both substrate and ligand; however, more complex substituents were not found to be correlative. Here, we review and compare the steric parameters common in quantitative structure activity relationships (QSAR), a common method for pharmaceutical function optimization, and how they might be applied in asymmetric catalysis, as the two fields are undeniably similar. We re-evaluate steric/enantioselection relationships, which we previously analysed with Charton steric parameters, using the more sophisticated Sterimol parameters developed by Verloop and co-workers in a QSAR context. Use of these Sterimol parameters led to strong correlations in numerous processes where Charton parameters had previously failed. Sterimol parameterization also allows for greater mechanistic insight into the key elements of asymmetric induction within these systems.

The delineation of molecular properties that underlie reactivity and selectivity is at the core of physical organic chemistry, and this knowledge can be used to inform the design of improved synthetic methods or identify new chemical transformations. For this reason, the mathematical representation of properties affecting reactivity and selectivity trends, that is, molecular parameters, is paramount. Correlations produced by equating these molecular parameters with experimental outcomes are often defined as free-energy relationships and can be used to evaluate the origin of selectivity and to generate new, experimentally testable hypotheses. The premise behind successful correlations of this type is that a systematically perturbed molecular property affects a transition-state interaction between the catalyst, substrate and any reaction components involved in the determination of selectivity. Classic physical organic molecular descriptors, such as Hammett, Taft or Charton parameters, seek to independently probe isolated electronic or steric effects. However, these parameters cannot address simultaneous, non-additive variations to more than one molecular property, which limits their utility. Here we report a parameter system based on the vibrational response of a molecule to infrared radiation that can be used to mathematically model and predict selectivity trends for reactions with interlinked steric and electronic effects at positions of interest. The disclosed parameter system is mechanistically derived and should find broad use in the study of chemical and biological systems.

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Predicting site selectivity in C–H bond oxidation reactions involving heteroatom transfer is challenged by the small energetic differences between disparate bond types and the subtle interplay of steric and electronic effects that influence reactivity. Herein, the factors governing selective Rh2(esp)2-catalyzed C–H amination of isoamylbenzene derivatives are investigated, where modification to both the nitrogen source, a sulfamate ester, and substrate are shown to impact isomeric product ratios. Linear regression mathematical modeling is used to define a relationship that equates both IR stretching parameters and Hammett σ+ values to the differential free energy of benzylic versus tertiary C–H amination. This model has informed the development of a novel sulfamate ester, which affords the highest benzylic-to-tertiary site selectivity (9.5:1) observed for this system.

A broad series of fully characterized, well-defined silica-supported W metathesis catalysts with the general formula [(≡SiO)W(═NAr)(═CHCMe2R)(X)] (Ar = 2,6-iPr2C6H3 (AriPr), 2,6-Cl2C6H3 (ArCl), 2-CF3C6H4 (ArCF3), and C6F5 (ArF5); X = OC(CF3)3 (OtBuF9), OCMe(CF3)2 (OtBuF6), OtBu, OSi(OtBu)3, 2,5-dimethylpyrrolyl (Me2Pyr) and R = Me or Ph) was prepared by grafting bis-X substituted complexes [W(NAr)(═CHCMe2R)(X)2] on silica partially dehydroxylated at 700 °C (SiO2-(700)), and their activity was evaluated with the goal to obtain detailed structure-activity relationships. Quantitative influence of the ligand set on the activity (turnover frequency, TOF) in self-metathesis of cis-4-nonene was investigated using multivariate linear regression analysis tools. The TOF of these catalysts (activity) can be well predicted from simple steric and electronic parameters of the parent protonated ligands; it is described by the mutual contribution of the NBO charge of the nitrogen or the IR intensity of the symmetric N-H stretch of the ArNH2, corresponding to the imido ligand, together with the Sterimol B5 and pKa of HX, representing the X ligand. This quantitative and predictive structure-activity relationship analysis of well-defined heterogeneous catalysts shows that high activity is associated with the combination of X and NAr ligands of opposite electronic character and paves the way toward rational development of metathesis catalysts.