30 Years of Biotherapeutics Development-What Have We Learned?

Since the birth of biotechnology, hundreds of biotherapeutics have been developed and approved by the US Food and Drug Administration (FDA) for human use. These novel medicines not only bring significant benefit to patients but also represent precision tools to interrogate human disease biology. Accordingly, much has been learned from the successes and failures of hundreds of high-quality clinical trials. In this review, we discuss general and broadly applicable themes that have emerged from this collective experience. We base our discussion on insights gained from exploring some of the most important target classes, including interleukin-1 (IL-1), tumor necrosis factor α (TNF-α), IL-6, IL-12/23, IL-17, IL-4/13, IL-5, immunoglobulin E (IgE), integrins and B cells. We also describe current challenges and speculate about how emerging technological capabilities may enable the discovery and development of the next generation of biotherapeutics.

The Art and Science of Molecular Docking.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era.

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

An Overview of Microcrystal Electron Diffraction (MicroED).

The bedrock of drug discovery and a key tool for understanding cellular function and drug mechanisms of action is the structure determination of chemical compounds, peptides, and proteins. The development of new structure characterization tools, particularly those that fill critical gaps in existing methods, presents important steps forward for structural biology and drug discovery. The emergence of microcrystal electron diffraction (MicroED) expands the application of cryo-electron microscopy to include samples ranging from small molecules and membrane proteins to even large protein complexes using crystals that are one-billionth the size of those required for X-ray crystallography. This review outlines the conception, achievements, and exciting future trajectories for MicroED, an important addition to the existing biophysical toolkit.

The Form and Function of PIEZO2.

Mechanosensation is the ability to detect dynamic mechanical stimuli (e.g., pressure, stretch, and shear stress) and is essential for a wide variety of processes, including our sense of touch on the skin. How touch is detected and transduced at the molecular level has proved to be one of the great mysteries of sensory biology. A major breakthrough occurred in 2010 with the discovery of a family of mechanically gated ion channels that were coined PIEZOs. The last 10 years of investigation have provided a wealth of information about the functional roles and mechanisms of these molecules. Here we focus on PIEZO2, one of the two PIEZO proteins found in humans and other mammals. We review how work at the molecular, cellular, and systems levels over the past decade has transformed our understanding of touch and led to unexpected insights into other types of mechanosensation beyond the skin.

Psychedelic-inspired drug discovery using an engineered biosensor.

Ligands can induce G protein-coupled receptors (GPCRs) to adopt a myriad of conformations, many of which play critical roles in determining the activation of specific signaling cascades associated with distinct functional and behavioral consequences. For example, the 5-hydroxytryptamine 2A receptor (5-HT2AR) is the target of classic hallucinogens, atypical antipsychotics, and psychoplastogens. However, currently available methods are inadequate for directly assessing 5-HT2AR conformation both in vitro and in vivo. Here, we developed psychLight, a genetically encoded fluorescent sensor based on the 5-HT2AR structure. PsychLight detects behaviorally relevant serotonin release and correctly predicts the hallucinogenic behavioral effects of structurally similar 5-HT2AR ligands. We further used psychLight to identify a non-hallucinogenic psychedelic analog, which produced rapid-onset and long-lasting antidepressant-like effects after a single administration. The advent of psychLight will enable in vivo detection of serotonin dynamics, early identification of designer drugs of abuse, and the development of 5-HT2AR-dependent non-hallucinogenic therapeutics.

Ribonucleotide Reductases: Structure, Chemistry, and Metabolism Suggest New Therapeutic Targets.

Ribonucleotide reductases (RNRs) catalyze the de novo conversion of nucleotides to deoxynucleotides in all organisms, controlling their relative ratios and abundance. In doing so, they play an important role in fidelity of DNA replication and repair. RNRs' central role in nucleic acid metabolism has resulted in five therapeutics that inhibit human RNRs. In this review, we discuss the structural, dynamic, and mechanistic aspects of RNR activity and regulation, primarily for the human and Escherichia coli class Ia enzymes. The unusual radical-based organic chemistry of nucleotide reduction, the inorganic chemistry of the essential metallo-cofactor biosynthesis/maintenance, the transport of a radical over a long distance, and the dynamics of subunit interactions all present distinct entry points toward RNR inhibition that are relevant for drug discovery. We describe the current mechanistic understanding of small molecules that target different elements of RNR function, including downstream pathways that lead to cell cytotoxicity. We conclude by summarizing novel and emergent RNR targeting motifs for cancer and antibiotic therapeutics.

Click Chemistry in Proteomic Investigations.

Despite advances in genetic and proteomic techniques, a complete portrait of the proteome and its complement of dynamic interactions and modifications remains a lofty, and as of yet, unrealized, objective. Specifically, traditional biological and analytical approaches have not been able to address key questions relating to the interactions of proteins with small molecules, including drugs, drug candidates, metabolites, or protein post-translational modifications (PTMs). Fortunately, chemists have bridged this experimental gap through the creation of bioorthogonal reactions. These reactions allow for the incorporation of chemical groups with highly selective reactivity into small molecules or protein modifications without perturbing their biological function, enabling the selective installation of an analysis tag for downstream investigations. The introduction of chemical strategies to parse and enrich subsets of the "functional" proteome has empowered mass spectrometry (MS)-based methods to delve more deeply and precisely into the biochemical state of cells and its perturbations by small molecules. In this Primer, we discuss how one of the most versatile bioorthogonal reactions, "click chemistry", has been exploited to overcome limitations of biological approaches to enable the selective marking and functional investigation of critical protein-small-molecule interactions and PTMs in native biological environments.

The Science of Antibiotic Discovery.

We are experiencing an antimicrobial resistance (AMR) crisis, brought on by the drying up of the antibiotic discovery pipeline and the resulting unchecked spread of resistant pathogens. Traditional methods of screening environmental isolates or compound libraries have not produced a new drug in over 30 years. Antibiotic discovery is uniquely difficult due to a highly restrictive penetration barrier and other mechanisms that allow bacteria to survive in the presence of toxic compounds. In this Perspective, we analyze the challenges facing discovery and discuss an emerging new platform for antibiotic discovery. The penetration barrier makes screening conventional synthetic compound libraries largely impractical, and actinomycetes, the main source of natural product compounds, have been overmined. The emerging platform is based on understanding the rules that guide the permeation of molecules into bacteria and on advances in microbiology, which enable us to identify and access attractive groups of secondary metabolite producers. Establishing this platform will enable reliable production of lead compounds to combat AMR.

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

Lysine-Targeted Inhibitors and Chemoproteomic Probes.

Covalent inhibitors are widely used in drug discovery and chemical biology. Although covalent inhibitors are frequently designed to react with noncatalytic cysteines, many ligand binding sites lack an accessible cysteine. Here, we review recent advances in the chemical biology of lysine-targeted covalent inhibitors and chemoproteomic probes. By analyzing crystal structures of proteins bound to common metabolites and enzyme cofactors, we identify a large set of mostly unexplored lysines that are potentially targetable with covalent inhibitors. In addition, we describe mass spectrometry-based approaches for determining proteome-wide lysine ligandability and lysine-reactive chemoproteomic probes for assessing drug-target engagement. Finally, we discuss the design of amine-reactive inhibitors that form reversible covalent bonds with their protein targets.

DNA-Encoded Chemical Libraries: A Selection System Based on Endowing Organic Compounds with Amplifiable Information.

The discovery of organic ligands that bind specifically to proteins is a central problem in chemistry, biology, and the biomedical sciences. The encoding of individual organic molecules with distinctive DNA tags, serving as amplifiable identification bar codes, allows the construction and screening of combinatorial libraries of unprecedented size, thus facilitating the discovery of ligands to many different protein targets. Fundamentally, one links powers of genetics and chemical synthesis. After the initial description of DNA-encoded chemical libraries in 1992, several experimental embodiments of the technology have been reduced to practice. This review provides a historical account of important milestones in the development of DNA-encoded chemical libraries, a survey of relevant ongoing research activities, and a glimpse into the future.

Discovery of Next-Generation Antimicrobials through Bacterial Self-Screening of Surface-Displayed Peptide Libraries.

Peptides have great potential to combat antibiotic resistance. While many platforms can screen peptides for their ability to bind to target cells, there are virtually no platforms that directly assess the functionality of peptides. This limitation is exacerbated when identifying antimicrobial peptides because the phenotype, death, selects against itself and has caused a scientific bottleneck that confines research to a few naturally occurring classes of antimicrobial peptides. We have used this seeming dissonance to develop Surface Localized Antimicrobial Display (SLAY), a platform that allows screening of unlimited numbers of peptides of any length, composition, and structure in a single tube for antimicrobial activity. Using SLAY, we screened ∼800,000 random peptide sequences for antimicrobial function and identified thousands of active sequences, dramatically increasing the number of known antimicrobial sequences. SLAY hits present with different potential mechanisms of peptide action and access to areas of antimicrobial physicochemical space beyond what nature has evolved. VIDEO ABSTRACT.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells.

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Chemoproteomic discovery of a covalent allosteric inhibitor of WRN helicase.

WRN helicase is a promising target for treatment of cancers with microsatellite instability (MSI) due to its essential role in resolving deleterious non-canonical DNA structures that accumulate in cells with faulty mismatch repair mechanisms1-5. Currently there are no approved drugs directly targeting human DNA or RNA helicases, in part owing to the challenging nature of developing potent and selective compounds to this class of proteins. Here we describe the chemoproteomics-enabled discovery of a clinical-stage, covalent allosteric inhibitor of WRN, VVD-133214. This compound selectively engages a cysteine (C727) located in a region of the helicase domain subject to interdomain movement during DNA unwinding. VVD-133214 binds WRN protein cooperatively with nucleotide and stabilizes compact conformations lacking the dynamic flexibility necessary for proper helicase function, resulting in widespread double-stranded DNA breaks, nuclear swelling and cell death in MSI-high (MSI-H), but not in microsatellite-stable, cells. The compound was well tolerated in mice and led to robust tumour regression in multiple MSI-H colorectal cancer cell lines and patient-derived xenograft models. Our work shows an allosteric approach for inhibition of WRN function that circumvents competition from an endogenous ATP cofactor in cancer cells, and designates VVD-133214 as a promising drug candidate for patients with MSI-H cancers.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Computational approaches streamlining drug discovery.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities.

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.

Harnessing plant biosynthesis for the development of next-generation therapeutics.

Genomics-based predictions indicate that plants harbor the ability to make a vast array of as yet undiscovered chemistry. Recent advances open up the potential to harness this capability at unprecedented scale for the discovery and development of new therapeutics.