Oxygenating Biocatalysts for Hydroxyl Functionalisation in Drug Discovery and Development

Catalytic Asymmetric Oxidative Coupling between C(sp3)-H Bonds and Carboxylic Acids

The direct enantioselective functionalization of C(sp3)-H bonds in organic molecules could fundamentally transform the synthesis of chiral molecules. In particular, the enantioselective oxidation of these bonds would dramatically change the production methods of chiral alcohols and esters, which are prevalent in natural products, pharmaceuticals, and fine chemicals. Remarkable advances have been made in the enantioselective construction of carbon-carbon and carbon-nitrogen bonds through the C(sp3)-H bond functionalization. However, the direct enantioselective formation of carbon-oxygen bonds from C(sp3)-H bonds remains a considerable challenge. We herein report a highly enantioselective C(sp3)-H bond oxidative coupling with carboxylic acids. The method applies to allylic and propargylic C-H bonds and employs various carboxylic acids as oxygenating agents. The method successfully synthesized a range of chiral esters directly from readily available alkenes and alkynes, greatly simplifying the synthesis of chiral esters and related alcohols.

Synergizing quantum techniques with machine learning for advancing drug discovery challenge

The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques. Participants utilized the IBM Qiskit platform within the constraints of the Noisy Intermediate Scale Quantum (NISQ) era, characterized by noise and limited quantum computing resources. The contest emphasized the importance of accurate estimation, efficient use of quantum resources, and the integration of machine learning techniques. This competition highlighted the potential of hybrid classical-quantum frameworks and machine learning in advancing quantum computing for practical applications, particularly in drug discovery.

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based self-supervised machine learning framework, MutComputeX, with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher expression yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40 % improvement in the TTN (218±3) as compared to WT LDO (TTN=160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants

Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.

Predictive Minisci late stage functionalization with transfer learning

Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms.

Enzymatic synthesis of pharmacologically relevant chiral sulfoxides by improved CbBVMO variants

Baeyer-Villiger monooxygenases (BVMOs) are able to catalyse the asymmetric oxidation of sulfides. This property has made them attractive catalysts for the synthesis of chiral sulfoxide drugs. Here, we have designed and synthesised an exhaustive combinatorial mutant library of the previously identified lansoprazole sulfide monooxygenase CbBVMOV1. From this synthetic combinatorial mutant library, the best mutant, CbBVMOV3, was selected with a specific activity of approximately 1 U mg-1 for lansoprazole sulfoxides. We then optimised the reaction conditions of a two-phase system, achieving the enzymatic asymmetric synthesis of (R)-lansoprazole in a space-time yield of 213 g L-1 d-1 and an enantiomeric excess of >99% (R) with no detectable by-products. In addition, CbBVMOV3 showed higher activity towards other prazole sulfides. These results indicate the potential application of CbBVMO in the chiral sulfoxide drug industry.

Versatile Biocatalytic C(sp3 )-H Oxyfunctionalization for the Site- Selective and Stereodivergent Synthesis of α- and β-Hydroxy Acids

C(sp3 )-H oxyfunctionalization, the insertion of an O-atom into C(sp3 )-H bonds, streamlines the synthesis of complex molecules from easily accessible precursors and represents one of the most challenging tasks in organic chemistry with regard to site and stereoselectivity. Biocatalytic C(sp3 )-H oxyfunctionalization has the potential to overcome limitations inherent to small-molecule-mediated approaches by delivering catalyst-controlled selectivity. Through enzyme repurposing and activity profiling of natural variants, we have developed a subfamily of α-ketoglutarate-dependent iron dioxygenases that catalyze the site- and stereodivergent oxyfunctionalization of secondary and tertiary C(sp3 )-H bonds, providing concise synthetic routes towards four types of 92 α- and β-hydroxy acids with high efficiency and selectivity. This method provides a biocatalytic approach for the production of valuable but synthetically challenging chiral hydroxy acid building blocks.

Unusual Biotransformation Reactions of Drugs and Drug Candidates

Detailed assessment of the fate of drugs in nonclinical test species and humans is essential to ensure the safety and efficacy of medicines in patients. In this context, biotransformation of drugs and drug candidates has been an area of keen interest over many decades in the pharmaceutical industry as well as academia. Although many of the enzymes and biotransformation pathways involved in the metabolism of xenobiotics and more specifically drugs have been well characterized, each drug molecule is unique and constitutes specific challenges for the biotransformation scientist. In this mini-review written for the special issue on the occasion of the 50th Anniversary celebration of Drug Metabolism and Disposition and to celebrate contributions of F. Peter Guengerich, one of the pioneers of the drug metabolism field, recently reported "unusual" biotransformation reactions are presented. Scientific and technological advances in the "toolbox" of the biotransformation scientists are summarized. As the pharmaceutical industry continues to explore therapeutic modalities different from the traditional small molecule drugs, the new challenges confronting the biotransformation scientist as well as future opportunities are discussed. SIGNIFICANCE STATEMENT: For the biotransformation scientists, it is essential to share and be aware of unexpected biotransformation reactions so that they can increase their confidence in predicting metabolites of drugs in humans to ensure the safety and efficacy of these metabolites before the medicines reach large numbers of patients. The purpose of this review is to highlight recent observations of "unusual" metabolites so that the scientists working in the area of drug metabolism can strengthen their readiness in expecting the unexpected.

Opportunities for Accelerating Drug Discovery and Development by Using Engineered Drug-Metabolizing Enzymes

The study of drug metabolism is fundamental to drug discovery and development (DDD) since by mediating the clearance of most drugs, metabolic enzymes influence their bioavailability and duration of action. Biotransformation can also produce pharmacologically active or toxic products, which complicates the evaluation of the therapeutic benefit versus liability of potential drugs but also provides opportunities to explore the chemical space around a lead. The structures and relative abundance of metabolites are determined by the substrate and reaction specificity of biotransformation enzymes and their catalytic efficiency. Preclinical drug biotransformation studies are done to quantify in vitro intrinsic clearance to estimate likely in vivo pharmacokinetic parameters, to predict an appropriate dose, and to anticipate interindividual variability in response, including from drug-drug interactions. Such studies need to be done rapidly and cheaply, but native enzymes, especially in microsomes or hepatocytes, do not always produce the full complement of metabolites seen in extrahepatic tissues or preclinical test species. Furthermore, yields of metabolites are usually limiting. Engineered recombinant enzymes can make DDD more comprehensive and systematic. Additionally, as renewable, sustainable, and scalable resources, they can also be used for elegant chemoenzymatic, synthetic approaches to optimize or synthesize candidates as well as metabolites. Here, we will explore how these new tools can be used to enhance the speed and efficiency of DDD pipelines and provide a perspective on what will be possible in the future. The focus will be on cytochrome P450 enzymes to illustrate paradigms that can be extended in due course to other drug-metabolizing enzymes. SIGNIFICANCE STATEMENT: Protein engineering can generate enhanced versions of drug-metabolizing enzymes that are more stable, better suited to industrial conditions, and have altered catalytic activities, including catalyzing non-natural reactions on structurally complex lead candidates. When applied to drugs in development, libraries of engineered cytochrome P450 enzymes can accelerate the identification of active or toxic metabolites, help elucidate structure activity relationships, and, when combined with other synthetic approaches, provide access to novel structures by regio- and stereoselective functionalization of lead compounds.