The dynamics of drug-target interactions: drug-target residence time and its impact on efficacy and safety

Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5

By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.

A novel fully human anti-NT-ANGPTL3 antibody from phage display library exhibits potent ApoB, TG, and LDL-C lowering activities in hyperlipidemia mice

Dyslipidemia is characterized by elevated plasma levels of low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and TG-rich lipoprotein (TGRLs) in circulation, and is closely associated with the incidence and development of cardiovascular disease. Angiopoietin-like protein 3 (ANGPTL3) deficiency has been identified as a cause of familial combined hypolipidemia in humans, which allows it to be an important therapeutic target for reducing plasma lipids. Here, we report the discovery and characterization of a novel fully human antibody F1519-D95aA against N-terminal ANGPTL3 (NT-ANGPTL3), which potently inhibits NT-ANGPTL3 with a KD as low as 9.21 nM. In hyperlipidemic mice, F1519-D95aA shows higher apolipoprotein B (ApoB) and TG-lowering, and similar LDL-C reducing activity as compared to positive control Evinacumab (56.50% vs 26.01% decrease in serum ApoB levels, 30.84% vs 25.28% decrease in serum TG levels, 23.32% vs 22.52% decrease in serum LDLC levels, relative to vehicle group). Molecular docking and binding energy calculations reveal that the F1519-D95aA-ANGPTL3 complex (10 hydrogen bonds, -65.51 kcal/mol) is more stable than the Evinacumab-ANGPTL3 complex (4 hydrogen bonds, -63.76 kcal/mol). Importantly, F1519-D95aA binds to ANGPTL3 with different residues in ANGPTL3 from Evinacumab, suggesting that F1519-D95aA may be useful for the treatment of patients resistant to Evinacumab. In conclusion, F1519-D95aA is a novel fully human anti-NT-ANGPTL3 antibody with potent plasma ApoB, TG, and LDL-C lowering activities, which can potentially serve as a therapeutic agent for hyperlipidemia and relevant cardiovascular diseases.

High-throughput analysis system of interaction kinetics for data-driven antibody design

Surface plasmon resonance (SPR) is widely used for antigen-antibody interaction kinetics analysis. However, it has not been used in the screening phase because of the low throughput of measurement and analysis. Herein, we proposed a high-throughput SPR analysis system named "BreviA" using the Brevibacillus expression system. Brevibacillus was transformed using a plasmid library containing various antibody sequences, and single colonies were cultured in 96-well plates. Sequence analysis was performed using bacterial cells, and recombinant antibodies secreted in the supernatant were immobilized on a sensor chip to analyze their interactions with antigens using high-throughput SPR. Using this system, the process from the transformation to 384 interaction analyses can be performed within a week. This system utility was tested using an interspecies specificity design of an anti-human programmed cell death protein 1 (PD-1) antibody. A plasmid library containing alanine and tyrosine mutants of all complementarity-determining region residues was generated. A high-throughput SPR analysis was performed against human and mouse PD-1, showing that the mutation in the specific region enhanced the affinity for mouse PD-1. Furthermore, deep mutational scanning of the region revealed two mutants with > 100-fold increased affinity for mouse PD-1, demonstrating the potential efficacy of antibody design using data-driven approach.

Evolution of Slow-Binding Inhibitors Targeting Histone Deacetylase Isoforms

Because the overexpression of histone deacetylase enzymes (HDACs) has been linked to numerous diseases, including various cancers and neurodegenerative disorders, HDAC inhibitors have emerged as promising therapeutic agents. However, most HDAC inhibitors lack both subclass and isoform selectivity, which leads to potential toxicity. Unlike classical hydroxamate HDAC inhibitors, slow-binding HDAC inhibitors form tight and prolonged bonds with HDAC enzymes. This distinct mechanism of action improves both selectivity and toxicity profiles, which makes slow-binding HDAC inhibitors a promising class of therapeutic agents for various diseases. Therefore, the development of slow-binding HDAC inhibitors that can effectively target a wide range of HDAC isoforms is crucial. This Perspective provides valuable insights into the potential and progress of slow-binding HDAC inhibitors as promising drug candidates for the treatment of various diseases.

Non-equilibrium modalities of inhibition: Characterizing irreversible inhibition for the ErbB receptor family members

Most drug target interactions for clinically approved small-molecules are non-equilibrium slow-onset, tight-binding or irreversible in nature, with pronounced element of time-dependence of inhibition. Analysis of such modality of inhibition requires a continuous enzyme kinetic measurement that can yield complete progress curves and an automated high-throughput analysis pipeline. Given the increasing emphasis on designing non-equilibrium modes of inhibiting an enzyme target (especially irreversible), the above specified pipeline for data generation and analysis is essential for extracting parameters to guide decisions in early drug discovery. In this manuscript, the methodology and data analysis protocol from our irreversible inhibitor characterization campaigns for the ErbB receptor family members is presented. Guidance is provided on the appropriate design of assay to generate quality data, setting up the analysis and estimation of inactivation rate (kinact) and the pseudo-equilibrium binding affinity (KI) constant (or their ratio kinact/KI) in a high-throughput manner for the inhibitor interacting with the protein target of interest.

Continuous enzyme activity assay for high-throughput classification of histone deacetylase 8 inhibitors

Aim

Human histone deacetylase 8 (KDAC8) is a well-recognized pharmaceutical target in Cornelia de Lange syndrome and different types of cancer, particularly childhood neuroblastoma. Several classes of chemotypes have been identified, which interfere with the enzyme activity of KDAC8. These compounds have been identified under equilibrium or near equilibrium conditions for inhibitor binding to the target enzyme. This study aims for the classification of KDAC8 inhibitors according to the mode of action and identification of most promising lead compounds for drug development.

Methods

A continuous enzyme activity assay is used to monitor inhibition kinetics.

Results

A high-throughput continuous KDAC8 activity assay is developed that provides additional mechanistic information about enzyme inhibition enabling the classification of KDAC8 inhibitors according to their mode of action. Fast reversible inhibitors act as a molecular chaperone and are capable to rescue the enzyme activity of misfolded KDAC8, while covalent inactivators and slow dissociating inhibitors do not preserve KDAC8 activity.

Conclusions

The application of continuous KDAC8 activity assay reveals additional information about the mode of interaction with inhibitors, which can be used to classify KDAC8 inhibitors according to their mode of action. The approach is compatible with the high-throughput screening of compound libraries. Fast reversible inhibitors of KDAC8 act as molecular chaperones and recover enzyme activity from misfolded protein conformations. In contrast, slow-binding inhibitors and covalent inactivators of KDAC8 are not capable to recover enzyme activity.

Competition in drug binding and … the race to equilibrium

Binding kinetics has become a popular topic in pharmacology due to its potential contribution to the selectivity and duration of drug action. Yet, the overall kinetic aspects of complex binding mechanisms are still merely described in terms of elaborate algebraic equations. Interestingly, it has been recommended some 10 years ago to examine such mechanisms in terms of binding fluxes instead of the conventional rate constants. Alike the velocity of product formation in enzymology, those fluxes refer to the velocity by which one target species converts into another one. Novel binding flux-based approaches are utilized to get a better visual insight into the "competition" between two drugs/ligands for a single target as well as between induced fit- and conformational selection pathways for a single ligand within a thermodynamic cycle. The present data were obtained by differential equation-based simulations. Early on, the ligand-binding steps "race" to equilibrium (i.e., when their forward and reverse fluxes are equal) at their individual pace. The overall/global equilibrium is only reached later on. For the competition association assays, this parting might produce a transient "overshoot" of one of the bound target species. A similar overshoot may also show up within a thermodynamic cycle and, at first glance, suggest that the induced fit pathway dominates. Yet, present findings show that under certain circumstances, it could rather be the other way round. Novel binding flux-based approaches offer visually attractive insights into crucial aspects of "complex" binding mechanisms under non-equilibrium conditions.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Estimation of Drug-Target Residence Time by Targeted Molecular Dynamics Simulations

Drug-target residence time has emerged as a key selection factor in drug discovery since the binding duration of a drug molecule to its protein target can significantly impact its in vivo efficacy. The challenge in studying the residence time, in early drug discovery stages, lies in how to cost-effectively determine the residence time for the systematic assessment of compounds. Currently, there is still a lack of computational protocols to quickly estimate such a measure, particularly for large and flexible protein targets and drugs. Here, we report an efficient computational protocol, based on targeted molecular dynamics, to rank drug candidates by their residence time and to obtain insights into ligand-target dissociation mechanisms. The method was assessed on a dataset of 10 arylpyrazole inhibitors of CDK8, a large, flexible, and clinically important target, for which the experimental residence time of the inhibitors ranges from minutes to hours. The compounds were correctly ranked according to their estimated residence time scores compared to their experimental values. The analysis of protein-ligand interactions along the dissociation trajectories highlighted the favorable contribution of hydrophobic contacts to residence time and revealed key residues that strongly affect compound residence time.

Role of Half-of-Sites Reactivity and Inter-Subunit Communications in DAHP Synthase Catalysis and Regulation

α-Carboxyketose synthases, including 3-deoxy-d-arabinoheptulosonate 7-phosphate synthase (DAHPS), are long-standing targets for inhibition. They are challenging targets to create tight-binding inhibitors against, and inhibitors often display half-of-sites binding and partial inhibition. Half-of-sites inhibition demonstrates the existence of inter-subunit communication in DAHPS. We used X-ray crystallography and spatially resolved hydrogen-deuterium exchange (HDX) to reveal the structural and dynamic bases for inter-subunit communication in Escherichia coli DAHPS(Phe), the isozyme that is feedback-inhibited by phenylalanine. Crystal structures of this homotetrameric (dimer-of-dimers) enzyme are invariant over 91% of its sequence. Three variable loops make up 8% of the sequence and are all involved in inter-subunit contacts across the tight-dimer interface. The structures have pseudo-twofold symmetry indicative of inter-subunit communication across the loose-dimer interface, with the diagonal subunits B and C always having the same conformation as each other, while subunits A and D are variable. Spatially resolved HDX reveals contrasting responses to ligand binding, which, in turn, affect binding of the second substrate, erythrose-4-phosphate (E4P). The N-terminal peptide, M1-E12, and the active site loop that binds E4P, F95-K105, are key parts of the communication network. Inter-subunit communication appears to have a catalytic role in all α-carboxyketose synthase families and a regulatory role in some members.

Label-Free Quantification of Molecular Interaction in Live Red Blood Cells by Tracking Nanometer Scale Membrane Fluctuations

Molecular interactions in live cells play an important role in both cellular functions and drug discovery. Current methods for measuring binding kinetics involve extracting the membrane protein and labeling, while the in situ quantification of molecular interaction with surface plasmon resonance (SPR) imaging mainly worked with fixed cells due to the micro-motion related noises of live cells. Here, an optical imaging method is presented to measure the molecular interaction with live red blood cells by tracking the nanometer membrane fluctuations. The membrane fluctuation dynamics are measured by tracking the membrane displacement during glycoprotein interaction. The data are analyzed with a thermodynamic model to determine the elastic properties of the cell observing reduced membrane fluctuations under fixatives, indicating cell fixations affect membrane mechanical properties. The binding kinetics of glycoprotein to several lectins are obtained by tracking the membrane fluctuation amplitude changes on single live cells. The binding kinetics and strength of different lectins are quite different, indicating the glycoproteins expression heterogeneity in single cells. It is anticipated that the method will contribute to the understanding of mechanisms of cell interaction and communication, and have potential applications in the mechanical assessment of cancer or other diseases at the single-cell level, and screening of membrane protein targeting drugs.

Strategies to Reduce the On-Target Platelet Toxicity of Bcl-xL Inhibitors: PROTACs, SNIPERs and Prodrug-Based Approaches

Apoptosis is a highly regulated cellular process. Aberration in apoptosis is a common characteristic of various disorders. Therefore, proteins involved in apoptosis are prime targets in multiple therapies. Bcl-xL is an antiapoptotic protein. Compared to other antiapoptotic proteins, the expression of Bcl-xL is common in solid tumors and, to an extent, in some leukemias and lymphomas. The overexpression of Bcl-xL is also linked to survival and chemoresistance in cancer and senescent cells. Therefore, Bcl-xL is a promising anticancer and senolytic target. Various nanomolar range Bcl-xL inhibitors have been developed. ABT-263 was successfully identified as a Bcl-xL /Bcl-2 dual inhibitor. But it failed in the clinical trial (phase-II) because of its on-target platelet toxicity, which also implies an essential role of Bcl-xL protein in the survival of human platelets. Classical Bcl-xL inhibitor designs utilize occupancy-driven pharmacology with typical shortcomings (such as dose-dependent off-target and on-target platelet toxicities). Hence, event-driven pharmacology-based approaches, such as proteolysis targeting chimeras (PROTACs) and SNIPERs (specific non-genetic IAP-based protein erasers) have been developed. The development of Bcl-xL based PROTACs was expected, as 600 E3-ligases are available in humans, while some (such as cereblon (CRBN), von Hippel-Lindau (VHL)) are relatively less expressed in platelets. Therefore, E3 ligase ligand-based Bcl-xL PROTACs (CRBN: XZ424, XZ739; VHL: DT2216, PZ703b, 753b) showed a significant improvement in platelet therapeutic index than their parent molecules (ABT-263: DT2216, PZ703b, 753b, XZ739, PZ15227; A1155463: XZ424). Other than their distinctive pharmacology, PROTACs are molecularly large, which limits their cell permeability and plays a role in improving their cell selectivity. We also discuss prodrug-based approaches, such as antibody-drug conjugates (ABBV-155), phosphate prodrugs (APG-1252), dendrimer conjugate (AZD0466), and glycosylated conjugates (Nav-Gal). Studies of in-vitro, in-vivo, structure-activity relationships, biophysical characterization, and status of preclinical/clinical inhibitors derived from these strategies are also discussed in the review.

A Comprehensive Guide for Assessing Covalent Inhibition in Enzymatic Assays Illustrated with Kinetic Simulations

Covalent inhibition has become more accepted in the past two decades, as illustrated by the clinical approval of several irreversible inhibitors designed to covalently modify their target. Elucidation of the structure-activity relationship and potency of such inhibitors requires a detailed kinetic evaluation. Here, we elucidate the relationship between the experimental read-out and the underlying inhibitor binding kinetics. Interactive kinetic simulation scripts are employed to highlight the effects of in vitro enzyme activity assay conditions and inhibitor binding mode, thereby showcasing which assumptions and corrections are crucial. Four stepwise protocols to assess the biochemical potency of (ir)reversible covalent enzyme inhibitors targeting a nucleophilic active site residue are included, with accompanying data analysis tailored to the covalent binding mode. Together, this will serve as a guide to make an educated decision regarding the most suitable method to assess covalent inhibition potency. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol I: Progress curve analysis of substrate association competition Basic Data Analysis Protocol 1A: Two-step irreversible covalent inhibition Basic Data Analysis Protocol 1B: One-step irreversible covalent inhibition Basic Data Analysis Protocol 1C: Two-step reversible covalent inhibition Basic Data Analysis Protocol 1D: Two-step irreversible covalent inhibition with substrate depletion Basic Protocol II: Incubation time-dependent potency IC₅₀ (t) Basic Data Analysis Protocol 2: Two-step irreversible covalent inhibition Basic Protocol III: Preincubation time-dependent inhibition without dilution Basic Data Analysis Protocol 3: Preincubation time-dependent inhibition without dilution Basic Data Analysis Protocol 3Ai: Two-step irreversible covalent inhibition Alternative Data Analysis Protocol 3Aii: Two-step irreversible covalent inhibition Basic Data Analysis Protocol 3Bi: One-step irreversible covalent inhibition Alternative Data Analysis Protocol 3Bii: One-step irreversible covalent inhibition Basic Data Analysis Protocol 3C: Two-step reversible covalent inhibition Basic Protocol IV: Preincubation time-dependent inhibition with dilution/competition Basic Data Analysis Protocol 4: Preincubation time-dependent inhibition with dilution Basic Data Analysis Protocol 4Ai: Two-step irreversible covalent inhibition Alternative Data Analysis Protocol 4Aii: Two-step irreversible covalent inhibition Basic Data Analysis Protocol 4Bi: One-step irreversible covalent inhibition Alternative Data Analysis Protocol 4Bii: One-step irreversible covalent inhibition.

Identification of Slow-Binding Inhibitors of the BoNT/A Protease

Botulinum neurotoxin A (BoNT/A) is a lethal toxin, which causes botulism, and is categorized as a bioterrorism threat, which causes flaccid paralysis and death. Botulinum A neurotoxicity is governed through its light chain (LC), a zinc metalloprotease. Pharmacological investigations aimed at negating BoNT/A's LC have typically looked to inhibitors that have been shown to inhibit the light chain's activity by reversible zinc chelation within its active site. This report outlines the first examples of nonpeptidic inhibitors of the BoNT/A LC that possess slow-binding kinetics, a needed logic to counteract the longevity of BoNT/A. Cyclopropane, alkyl, and alkenyl derivatives of 2,4-dichlorocinamic hydroxamic acid (DCHA) were shown to possess both one-step and two-step slow-binding kinetics. Structure-kinetic relationships (SKRs) were observed and were rationalized with the aid of docking models that predicted improved interactions with residues within a hydrophobic cleft adjacent to the active site.

A guide to enzyme kinetics in early drug discovery

Drugs interact with their target of interest to bring about the desired phenotypic outcome that results in disease alleviation. Traditionally, most lead optimization exercises were driven by affinity measures (like IC₅₀ ) to inform structure-activity relationship (SAR)-guided medicinal chemistry. However, an IC₅₀ value is a thermodynamic estimate measured under equilibrium conditions that can vary as a function of substrate concentration and/or time (the latter especially for nonequilibrium modalities). Further, like other thermodynamic estimates, it is a state-function that is indifferent to the path traversed from the initial state to the final state. This can be a cause for concern in drug discovery given the predominance of nonequilibrium interactions and the open thermodynamic nature of the human system. Under such situations, employing rates along with equilibrium constants (or IC₅₀ values) would be far more relevant to capture the time evolution of the small molecule's interaction with the target of interest. These rates are generally typified by the rate of association, rate of dissociation and the residence time of the small molecule on the target (target occupancy). These parameters, when combined with the concept of target vulnerability, therapeutic window, pharmacokinetic profile of the small molecule, estimates of endogenous ligand and target turnover, will shed critical insights into the kinetics and dynamics of a small molecule's interaction with the protein, and allow realistic modelling of the system to enable optimizations and dosing decisions. With that aim, this guide will attempt to introduce the traditional role of mechanistic enzymology within drug discovery and emphasize the importance of kinetics in guiding SAR-based optimizations. It will also present initial ideas on how kinetic investigation should be positioned relative to the temporal span of a drug-discovery pipeline to leverage maximal utility from the investment in time and effort.

Development of an intracellular quantitative assay to measure compound binding kinetics

Contemporary drug discovery typically quantifies the effect of a molecule on a biological target using the equilibrium-derived measurements of IC50, EC50, or KD. Kinetic descriptors of drug binding are frequently linked with the effectiveness of a molecule in modulating a disease phenotype; however, these parameters are yet to be fully adopted in early drug discovery. Nanoluciferase bioluminescence resonance energy transfer (NanoBRET) can be used to measure interactions between fluorophore-conjugated probes and luciferase fused target proteins. Here, we describe an intracellular NanoBRET competition assay that can be used to quantify cellular kinetic rates of compound binding to nanoluciferase-fused bromodomain and extra-terminal (BET) proteins. Comparative rates are generated using a cell-free NanoBRET assay and by utilizing orthogonal recombinant protein-based methodologies. A screen of known pan-BET inhibitors is used to demonstrate the value of this approach in the investigation of kinetic selectivity between closely related proteins.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Generation of a Novel High-Affinity Antibody Binding to PCSK9 Catalytic Domain with Slow Dissociation Rate by CDR-Grafting, Alanine Scanning and Saturated Site-Directed Mutagenesis for Favorably Treating Hypercholesterolemia

Inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) has become an attractive therapeutic strategy for lowering low-density lipoprotein cholesterol (LDL-C). In this study, a novel high affinity humanized IgG1 mAb (named h5E12-L230G) targeting the catalytic domain of human PCSK9 (hPCSK9) was generated by using CDR-grafting, alanine-scanning mutagenesis, and saturated site-directed mutagenesis. The heavy-chain constant region of h5E12-L230G was modified to eliminate the cytotoxic effector functions and mitigate the heterogeneity. The biolayer interferometry (BLI) binding assay and molecular docking study revealed that h5E12-L230G binds to the catalytic domain of hPCSK9 with nanomolar affinity (K_D = 1.72 nM) and an extremely slow dissociation rate (k_off, 4.84 × 10⁻⁵ s⁻¹), which interprets its quite low binding energy (−54.97 kcal/mol) with hPCSK9. Additionally, h5E12-L230G elevated the levels of LDLR and enhanced the LDL-C uptake in HepG2 cells, as well as reducing the serum LDL-C and total cholesterol (TC) levels in hyperlipidemic mouse model with high potency comparable to the positive control alirocumab. Our data indicate that h5E12-L230G is a high-affinity anti-PCSK9 antibody candidate with an extremely slow dissociation rate for favorably treating hypercholesterolemia and relevant cardiovascular diseases.

Induced fit versus conformational selection: From rate constants to fluxes… and back to rate constants

Induced fit- (IF) and conformational selection (CS) binding mechanisms have long been regarded to be mutually exclusive. Yet, they are now increasingly considered to produce the final ligand-target complex alongside within a thermodynamic cycle. This viewpoint benefited from the introduction of binding fluxes as a tool for analyzing the overall behavior of such cycle. This study aims to provide more vivid and applicable insights into this emerging field. In this respect, combining differential equation- based simulations and hitherto little explored alternative modes of calculation provide concordant information about the intricate workings of such cycle. In line with previous reports, we observe that the relative contribution of IF increases with the ligand concentration at equilibrium. Yet the baseline contribution may vary from one case to another and simulations as well as calculations show that this parameter is essentially regulated by the dissociation rate of both pathways. Closer attention should be paid to how the contributions of IF and CS compare at physiologically relevant drug/ligand concentrations. To this end, a simple equation discloses how changing a limited set of "microscopic" rate constants can extend the concentration range at which CS contributes most effectively. Finally, it could also be beneficial to extend the utilization of flux- based approaches to more physiologically relevant time scales and alternative binding models.

SAR study of small molecule inhibitors of the programmed cell death-1/programmed cell death-ligand 1 interaction

The development of small molecule inhibitors of programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) has drawn research interest for the treatment of cancer. Recently, we reported the discovery of a novel dimeric core small molecule PD-1/PD-L1 inhibitor. In an effort to discover more potent inhibitors, we further explored the dimeric core scaffold. Our investigations of the structure-activity-relationship revealed that introduction of lipophilic substituents onto one of the di-alkoxylated phenyl rings improved binding affinities to PD-L1, and inhibitory activities of PD-1/PD-L1 in cellular assays. Furthermore, conversion of the ether linker part to an olefin linker not only improved binding affinity but also led to slow dissociation binding kinetics. We also explored more potent, as well as downsized, scaffolds. Compounds bearing a linear chain in place of one of the di-alkoxylated phenyl rings exhibited good binding affinity with improved ligand efficiency (LE). Representative compounds demonstrated potent inhibitory activities of PD-1/PD-L1 in the submicromolar range in cellular assays as well as cellular function in the mixed lymphocyte reaction (MLR) assay with efficacy comparable to anti-PD-1 antibody. Our results provide applicable information for the design of more potent inhibitors targeting PD-1/PD-L1 pathway.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

Potent Phenylpyridine and Oxodihydrofuran Inhibitors of Cyclooxygenase-2: Optimization toward a Long Residence Time with Balanced Internal Energetics

Long residence time enzyme inhibitors with a two-step binding mechanism are characterized by a high internal energy barrier for target association. This raises the question of whether optimizing residence time via further increasing this internal energy barrier would inevitably lead to insufficient target occupancy in vivo due to slow, time-dependent binding. We attempted to address this question during optimization of cyclooxygenase-2 (COX-2) inhibitors. Defining long residence time drugs with acceptable association and dissociation rate constants required for sufficient target occupancy and sustained efficacy, which we termed "balanced internal energetics", provides an important criterion for successful progression during lead optimization. Despite the advancement of several COX-2 inhibitors to marketed drugs, their detailed inhibition kinetics have been surprisingly limiting especially during the structure-activity relationship process mainly due to the lack of robust kinetic assays. Herein, we describe a reoptimized COX enzymatic assay and a novel MS-based assay enabling detailed mechanistic studies for identifying long residence time COX-2 inhibitors with balanced internal energetics. These efforts led to the discovery of promising leads possessing dissociation half-lives of ≤40 h, much greater than the values of 6 and 0.71 h for two marketed drugs, etoricoxib and celecoxib, respectively. Importantly, the inhibition rate constants remain comparable to those of the marketed drugs and above the lower limits set by the criteria of balanced internal energetics, predicting sufficient target occupancy required for efficacy. Taken together, this study demonstrates the feasibility of increasing the internal energy barrier as a viable approach for lead optimization toward discovering long residence time drug candidates.

Evolution of the drug-target residence time model

IntroductionThe pharmacological action of a drug is linked to its affinity for a specific molecular target as quantified by in vitro equilibrium measurements. However, it is clear that for many highly effective drugs, interactions with their molecular targets do not conform to simple, equilibrium conditions in vivo and this results in a temporal discordance between pharmacokinetics and pharmacodynamics. The drug-target residence time model was developed to provide a theoretical framework with which to understand cases in which very slow dissociation of the drug-target complex in vivo results in durable PD effects even after systemic concentrations of drug have waned.Area coveredIn this article, the author provides a brief description of the drug-target residence time model and focuses on the refinements that have been made to the original model to incorporate the influences of compound rebinding in cells and pharmacokinetic properties of drug molecules.Expert opinionThere is now overwhelming evidence for the utility of the drug-target residence time model as a framework for understanding in vivo drug action. The in vitro measured residence time (τ_R) must be used in concert with equilibrium measures of drug-target affinity (e.g. IC₅₀) and with in vivo measures of pharmacokinetic half-life, to afford the researcher a powerful approach to compound optimization for clinical effect. Despite the significant use and refinement of this model, continued studies are required to better understand the dynamic interplay between residence time, target pathobiology, drug distribution and drug pharmacokinetics.

A Novel High-Throughput FLIPR Tetra-Based Method for Capturing Highly Confluent Kinetic Data for Structure-Kinetic Relationship Guided Early Drug Discovery

Target engagement by small molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationships. It is becoming clearer, however, that understanding the kinetics of the interaction between a small-molecule inhibitor and the biological target [structure-kinetic relationship (SKR)] is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in a high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in-depth kinetic studies are often carried out on only a small number of compounds, and usually at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process, but the throughput limitations of traditional methods preclude this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent data points for accurate parameter estimation from time course analysis. Here, we describe the use of the fluorescent imaging plate reader (FLIPR), a charge-coupled device (CCD) camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition, and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation. In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on hundreds of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.

Development of a novel, fully human, anti-PCSK9 antibody with potent hypolipidemic activity by utilizing phage display-based strategy

Background

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates serum LDL cholesterol (LDL-C) levels by facilitating the degradation of the LDL receptor (LDLR) and is an attractive therapeutic target for hypercholesterolemia intervention. Herein, we generated a novel fully human antibody with favourable druggability by utilizing phage display-based strategy.

Methods

A potent single-chain variable fragment (scFv) named AP2M21 was obtained by screening a fully human scFv phage display library with hPCSK9, and performing two in vitro affinity maturation processes including CDR-targeted tailored mutagenesis and cross-cloning. Thereafter, it was transformed to a full-length Fc-silenced anti-PCSK9 antibody FAP2M21 by fusing to a modified human IgG1 Fc fragment with L234A/L235A/N297G mutations and C-terminal lysine deletion, thus eliminating its immune effector functions and mitigating mAb heterogeneity.

Findings

Our data showed that the generated full-length anti-PCSK9 antibody FAP2M21 binds to hPCSK9 with a K_D as low as 1.42 nM, and a dramatically slow dissociation rate (k_off, 4.68 × 10⁻⁶ s⁻¹), which could be attributed to its lower binding energy (-47.51 kcal/mol) than its parent counterpart FAP2 (-30.39 kcal/mol). We verified that FAP2M21 potently inhibited PCSK9-induced reduction of LDL-C uptake in HepG2 cells, with an EC₅₀ of 43.56 nM. Further, in hPCSK9 overexpressed C57BL/6 mice, a single tail i.v. injection of FAP2M21 at 1, 3 and 10 mg/kg, dose-dependently up-regulated hepatic LDLR levels, and concomitantly reduced serum LDL-C by 3.3% (P = 0.658, unpaired Student's t-test), 30.2% (P = 0.002, Mann-Whitney U-test) and 37.2% (P = 0.002, Mann-Whitney U-test), respectively.

Interpretation

FAP2M21 with potent inhibitory effect on PCSK9 may serve as a promising therapeutic agent for treating hypercholesterolemia and associated cardiovascular diseases.

Milling of poorly soluble crystalline drug compounds to generate appropriate particle sizes for inhaled sustained drug delivery

One of the simplest design concepts of inhaled sustained drug delivery to the lung is to utilize the slow dissolution of drug crystals with poor aqueous solubility. An optimum dissolution rate, and thereby a delivery profile locally in the lung tissue, can be achieved in a reliable way by selecting a compound with an appropriate combination of solubility and particle size. It is in our experience relatively straightforward to manufacture monomodal particle size distributions of poorly soluble drug crystals in the mass median diameter range of either a few micrometers or a few hundred nanometers, but very challenging to manufacture a monomodal distribution in the range intermediate to these two. In this manuscript, we describe an investigation with the objective of generating desired particle sizes in the whole size range from a few micrometers to a few hundred nanometers for inhaled sustained drug delivery, by utilizing Adaptive Focused Acoustic (AFA) milling and planetary bead-milling. By combining the two different milling techniques it was possible to produce two to three distinctly different monomodal or almost monomodal particle size distributions in the desired particle size range of each of the model drug compounds in milligram scale. The dissolution kinetics of the different particle sizes of the model drugs were measured experimentally as well as predicted theoretically, showcasing that the dissolution kinetics can be characterized, predicted and significantly changed in a controlled way by modifying the particle size. For one of the model drugs, it was shown in an in vivo rat study that the inhaled sustained drug delivery profile in the lung tissue could be significantly changed by modifying the particle size of the drug.

Discovery of Roblitinib (FGF401) as a Reversible-Covalent Inhibitor of the Kinase Activity of Fibroblast Growth Factor Receptor 4

FGF19 signaling through the FGFR4/β-klotho receptor complex has been shown to be a key driver of growth and survival in a subset of hepatocellular carcinomas, making selective FGFR4 inhibition an attractive treatment opportunity. A kinome-wide sequence alignment highlighted a poorly conserved cysteine residue within the FGFR4 ATP-binding site at position 552, two positions beyond the gate-keeper residue. Several strategies for targeting this cysteine to identify FGFR4 selective inhibitor starting points are summarized which made use of both rational and unbiased screening approaches. The optimization of a 2-formylquinoline amide hit series is described in which the aldehyde makes a hemithioacetal reversible-covalent interaction with cysteine 552. Key challenges addressed during the optimization are improving the FGFR4 potency, metabolic stability, and solubility leading ultimately to the highly selective first-in-class clinical candidate roblitinib.

Slow-binding inhibitors of acetylcholinesterase of medical interest

Certain ligands slowly bind to acetylcholinesterase. As a result, there is a slow establishment of enzyme-inhibitor equilibrium characterized by a slow onset of inhibition prior reaching steady state. Three mechanisms account for slow-binding inhibition: a) slow binding rate constant k_on, b) slow ligand induced-fit following a fast binding step, c) slow conformational selection of an enzyme form. The slow equilibrium may be followed by a chemical step. This later that can be irreversible has been observed with certain alkylating agents and substrate transition state analogs. Slow-binding inhibitors present long residence times on target. This results in prolonged pharmacological or toxicological action. Through several well-known molecules (e.g. huperzine) and new examples (tocopherol, trifluoroacetophenone and a 6-methyluracil alkylammonium derivative), we show that slow-binding inhibitors of acetylcholinesterase are promising drugs for treatment of neurological diseases such as Alzheimer disease and myasthenia gravis. Moreover, they may be of interest for neuroprotection (prophylaxis) against organophosphorus poisoning. This article is part of the special issue entitled 'Acetylcholinesterase Inhibitors: From Bench to Bedside to Battlefield'.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

NeuNAc Oxime: A Slow-Binding and Effectively Irreversible Inhibitor of the Sialic Acid Synthase NeuB

NeuB is a bacterial sialic acid synthase used by neuroinvasive bacteria to synthesize N-acetylneuraminate (NeuNAc), helping them to evade the host immune system. NeuNAc oxime is a potent slow-binding NeuB inhibitor. It dissociated too slowly to be detected experimentally, with initial estimates of its residence time in the active site being >47 days. This is longer than the lifetime of a typical bacterial cell, meaning that inhibition is effectively irreversible. Inhibition data fitted well to a model that included a pre-equilibration step with a K_i of 36 μM, followed by effectively irreversible conversion to an E*·I complex, with a k₂ of 5.6 × 10^-5 s^-1. Thus, the inhibitor can subvert ligand release and achieve extraordinary residence times in spite of a relatively modest initial dissociation constant. The crystal structure showed the oxime functional group occupying the phosphate-binding site normally occupied by the substrate PEP and the tetrahedral intermediate. There was an ≈10% residual rate at high inhibitor concentrations regardless of how long NeuB and NeuNAc oxime were preincubated together. However, complete inhibition was achieved by incubating NeuNAc oxime with the actively catalyzing enzyme. This requirement for the enzyme to be actively turning over for the inhibitor to bind to the second subunit demonstrated an important role for intersubunit communication in the inhibitory mechanism.

Utilizing the Combination of Binding Kinetics and Micro-Pharmacokinetics Link in Vitro α-Glucosidase Inhibition to in Vivo Target Occupancy

Many compounds with good inhibitory activity (i.e., high affinity) within in vitro experiments failed in vivo studies due to a lack of efficacy from limited target occupancy (TO) in the drug discovery process. Recently, it was found that rate constants of the formation and dissociation of the binary drug-target complex, rather than affinity, often govern in vivo efficacy. Therefore, the binding kinetics (BK) properties of compound-target interaction are emerging as a pivotal parameter. However, it is obvious that BK rate constants of the compound against target would not be directly linked to the in vivo TO unless the compound concentration in the target vicinity at any time point (TPK) can be evaluated. Here, we developed a novel simulation model to quantitate the dynamic change of target engagement over time in rat with a combined use of BK and TPK features of Epicatechin gallate (ECG) and epigallocatechin gallate (EGCG) on the basis of α-glucosidase (AGH). Analysis of the results displayed that the percent of maximum AGH occupancies by the ECG were varied significantly from 48.9 to 95.3% and by the EGCG slightly from 96 to 99.8%; that the time course of above 70% engagement by ECG spanned a range from 0 to 0.64 h and by EGCG a range of 1.5 to 8.9 h in four different intestinal segments of the rat. It was clearly analyzed how each parameter in the simulation model effected on the in vivo the AGH engagement by ECG and EGCG. Our results provide a novel approach for assessing the potential inhibitory activity of the compounds against AGH.

REVO: Resampling of ensembles by variation optimization

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems, this occurs on prohibitively long time scales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here, we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as "trajectory variation" is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that resampling of ensembles by variation optimization will be a useful general tool to broadly explore free energy landscapes.

Physiologically Based Pharmacokinetic and Pharmacodynamic Analysis Enabled by Microfluidically Linked Organs-on-Chips

Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches are beginning to be integrated into drug development and approval processes because they enable key pharmacokinetic (PK) parameters to be predicted from in vitro data. However, these approaches are hampered by many limitations, including an inability to incorporate organ-specific differentials in drug clearance, distribution, and absorption that result from differences in cell uptake, transport, and metabolism. Moreover, such approaches are generally unable to provide insight into pharmacodynamic (PD) parameters. Recent development of microfluidic Organ-on-a-Chip (Organ Chip) cell culture devices that recapitulate tissue-tissue interfaces, vascular perfusion, and organ-level functionality offer the ability to overcome these limitations when multiple Organ Chips are linked via their endothelium-lined vascular channels. Here, we discuss successes and challenges in the use of existing culture models and vascularized Organ Chips for PBPK and PD modeling of human drug responses, as well as in vitro to in vivo extrapolation (IVIVE) of these results, and how these approaches might advance drug development and regulatory review processes in the future.

Determination of the binding mechanism of histone deacetylase inhibitors

This article places its focus on methods and tools enabling the elucidation of the mechanism by which ligands, small-molecule inhibitors, or substrates interact with zinc-containing bacterial or human members of the histone deacetylase family (HDACs). These methods include biochemical and biophysical approaches and can be subdivided into equilibrium and kinetic methods. More information about the exact mode of action can be obtained by combining these methods with specific mutant variants of the enzymes and/or series of structural similar ligands. All available equilibrium and kinetic data including additional information from 3D structures of HDAC-ligand complexes can be beneficially combined in a data analysis procedure called Integrated Global-Fit analysis eventually providing the most likely binding mechanism.

Drug-Target Association Kinetics in Drug Discovery

The important role of ligand-receptor binding kinetics in drug design and discovery is increasingly recognized by the drug research community. Over the past decade, accumulating evidence has shown that optimizing the ligand's dissociation rate constant can lead to desirable duration of in vivo target occupancy and, hence, improved pharmacodynamic properties. However, the association rate constant as a pharmacological principle remains less investigated, whereas it can play an equally important role in the selection of drug candidates. This review provides a compilation and discussion of otherwise scarce and dispersed information on this topic, bringing to light the importance of drug-target association in kinetics-directed drug design and discovery.

Drugging the Folate Pathway in Mycobacterium tuberculosis: The Role of Multi-targeting Agents

The folate biosynthetic pathway offers many druggable targets that have yet to be exploited in tuberculosis therapy. Herein, we have identified a series of small molecules that interrupt Mycobacterium tuberculosis (Mtb) folate metabolism by dual targeting of dihydrofolate reductase (DHFR), a key enzyme in the folate pathway, and its functional analog, Rv2671. We have also compared the antifolate activity of these compounds with that of para-aminosalicylic acid (PAS). We found that the bioactive metabolite of PAS, in addition to previously reported activity against DHFR, inhibits flavin-dependent thymidylate synthase in Mtb, suggesting a multi-targeted mechanism of action for this drug. Finally, we have shown that antifolate treatment in Mtb decreases the production of mycolic acids, most likely due to perturbation of the activated methyl cycle. We conclude that multi-targeting of the folate pathway in Mtb is associated with highly potent anti-mycobacterial activity.

Characterization of FGF401 as a reversible covalent inhibitor of fibroblast growth factor receptor 4

Biochemical and structural studies provide information on the mode of action of FGF401 as a selective, reversible covalent inhibitor of FGFR4.

Link between a high k on for drug binding and a fast clinical action: to be or not to be?

Review articles on binding kinetics essentially focus on drugs that dissociate slowly from their target since this is required for the successful treatment of many pathophysiological conditions. Recently, the therapeutic benefit of a high k on (i.e. the second order association rate constant) has also been linked to fast association and to a fast clinical action. Other studies, however, called this assertion into question since additional factors, like the dosing paradigm and the binding mechanism, are important as well. The still ongoing reticence about integrating binding kinetics in lead optimization programs motivated us to critically review the link between the drug's kinetic rate constants and their in vitro and in vivo target occupancy profile, with special focus on k on. The presented simulations tally with a positive link between a drug's effective/observed association rate (which is quite easy to determine in vitro) and the swiftness of its clinical action. On the other hand, the simulations show that the k on-concept should not be confounded with the effective association process since increasing this parameter only enhances the drug's in vitro and in vivo association under certain conditions: the binding mechanism should be suitable, rebinding (and thus the factors within the target's micro-environment that favour this mechanism) should not be too prominent and the dosage should not be kept in par with the drug's affinity. Otherwise, increasing k on could be ineffective or even be counter-productive.

KinITC-One Method Supports both Thermodynamic and Kinetic SARs as Exemplified on FimH Antagonists

Affinity data, such as dissociation constants (K_D ) or inhibitory concentrations (IC₅₀ ), are widely used in drug discovery. However, these parameters describe an equilibrium state, which is often not established in vivo due to pharmacokinetic effects and they are therefore not necessarily sufficient for evaluating drug efficacy. More accurate indicators for pharmacological activity are the kinetics of binding processes, as they shed light on the rate of formation of protein-ligand complexes and their half-life. Nonetheless, although highly desirable for medicinal chemistry programs, studies on structure-kinetic relationships (SKR) are still rare. With the recently introduced analytical tool kinITC this situation may change, since not only thermodynamic but also kinetic information of the binding process can be deduced from isothermal titration calorimetry (ITC) experiments. Using kinITC, ITC data of 29 mannosides binding to the bacterial adhesin FimH were re-analyzed to make their binding kinetics accessible. To validate these kinetic data, surface plasmon resonance (SPR) experiments were conducted. The kinetic analysis by kinITC revealed that the nanomolar affinities of the FimH antagonists arise from both (i) an optimized interaction between protein and ligand in the bound state (reduced off-rate constant k_off ) and (ii) a stabilization of the transition state or a destabilization of the unbound state (increased on-rate constant k_on ). Based on congeneric ligand modifications and structural input from co-crystal structures, a strong relationship between the formed hydrogen-bond network and k_off could be concluded, whereas electrostatic interactions and conformational restrictions upon binding were found to have mainly an impact on k_on .

Artificial intelligence in drug design

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.

A potent, proteolysis-resistant inhibitor of kallikrein-related peptidase 6 (KLK6) for cancer therapy, developed by combinatorial engineering

Human tissue kallikrein (KLK) proteases are hormone-like signaling molecules with important functions in cancer pathophysiology. KLK-related peptidase 6 (KLK6), specifically, is highly up-regulated in several types of cancer, where its increased activity promotes cancer invasion and metastasis. This characteristic suggests KLK6 as an attractive target for therapeutic interventions. However, inhibitors that specifically target KLK6 have not yet been reported, possibly because KLK6 shares a high sequence homology and structural similarity with other serine proteases and resists inhibition by many polypeptide inhibitors. Here, we present an innovative combinatorial approach to engineering KLK6 inhibitors via flow cytometry-based screening of a yeast-displayed mutant library of the human amyloid precursor protein Kunitz protease inhibitor domain (APPI), an inhibitor of other serine proteases, such as anionic and cationic trypsins. On the basis of this screening, we generated APPI_{M17L,I18F,S19F,F34V} (APPI-4M), an APPI variant with a KLK6 inhibition constant (K_i ) of 160 pm and a turnover time of 10 days. To the best of our knowledge, APPI-4M is the most potent KLK6 inhibitor reported to date, displaying 146-fold improved affinity and 13-fold improved proteolytic stability compared with WT APPI (APPI_WT). We further demonstrate that APPI-4M acts as a functional inhibitor in a cell-based model of KLK6-dependent breast cancer invasion. Finally, the crystal structures of the APPI_WT/KLK6 and APPI-4M/KLK6 complexes revealed the structural and mechanistic bases for the improved KLK6 binding and proteolytic resistance of APPI-4M. We anticipate that APPI-4M will have substantial translational potential as both imaging agent and therapeutic.

Structural and Kinetic Studies of the Potent Inhibition of Metallo-β-lactamases by 6-Phosphonomethylpyridine-2-carboxylates

There are currently no clinically available inhibitors of metallo-β-lactamases (MBLs), enzymes that hydrolyze β-lactam antibiotics and confer resistance to Gram-negative bacteria. Here we present 6-phosphonomethylpyridine-2-carboxylates (PMPCs) as potent inhibitors of subclass B1 (IMP-1, VIM-2, and NDM-1) and B3 (L1) MBLs. Inhibition followed a competitive, slow-binding model without an isomerization step (IC50 values of 0.3-7.2 μM; Ki values of 0.03-1.5 μM). Minimum inhibitory concentration assays demonstrated potentiation of β-lactam (Meropenem) activity against MBL-producing bacteria, including clinical isolates, at concentrations at which eukaryotic cells remain viable. Crystal structures revealed unprecedented modes of binding of inhibitor to B1 (IMP-1) and B3 (L1) MBLs. In IMP-1, binding does not replace the nucleophilic hydroxide, and the PMPC carboxylate and pyridine nitrogen interact closely (2.3 and 2.7 Å, respectively) with the Zn2 ion of the binuclear metal site. The phosphonate group makes limited interactions but is 2.6 Å from the nucleophilic hydroxide. Furthermore, the presence of a water molecule interacting with the PMPC phosphonate and pyridine N-C2 π-bond, as well as the nucleophilic hydroxide, suggests that the PMPC binds to the MBL active site as its hydrate. Binding is markedly different in L1, with the phosphonate displacing both Zn2, forming a monozinc enzyme, and the nucleophilic hydroxide, while also making multiple interactions with the protein main chain and Zn1. The carboxylate and pyridine nitrogen interact with Ser221 and -223, respectively (3 Å distance). The potency, low toxicity, cellular activity, and amenability to further modification of PMPCs indicate these and similar phosphonate compounds can be further considered for future MBL inhibitor development.

4-Amino-7,8-dihydro-1,6-naphthyridin-5(6 H)-ones as Inhaled Phosphodiesterase Type 4 (PDE4) Inhibitors: Structural Biology and Structure-Activity Relationships

Rational design of a novel template of naphthyridinones rapidly led to PDE4 inhibitors with subnanomolar enzymatic potencies. X-ray crystallography confirmed the binding mode of this novel template. We achieved compounds with double-digit picomolar enzymatic potencies through further structure-based design by targeting both the PDE4 enzyme metal-binding pocket and occupying the solvent-filled pocket. A strategy for lung retention and long duration of action based on low aqueous solubility was followed. In vivo efficacies were measured in a rat lung neutrophilia model by suspension microspray and dry powder administration. Suspension microspray of potent compounds showed in vivo efficacy with a clear dose-response. Despite sustained lung levels, dry powder administration performed much less well and without proper dose-response, highlighting clear differences between the two formulations. This indicates a deficiency in the low aqueous solubility strategy for long duration lung efficacy.

Discovery of 7-Oxo-2,4,5,7-tetrahydro-6 H-pyrazolo[3,4- c]pyridine Derivatives as Potent, Orally Available, and Brain-Penetrating Receptor Interacting Protein 1 (RIP1) Kinase Inhibitors: Analysis of Structure-Kinetic Relationships

We report the discovery of 7-oxo-2,4,5,7-tetrahydro-6 H-pyrazolo[3,4- c]pyridine derivatives as a novel class of receptor interacting protein 1 (RIP1) kinase inhibitors. On the basis of the overlay study between HTS hit 10 and GSK2982772 (6) in RIP1 kinase, we designed and synthesized a novel class of RIP1 kinase inhibitor 11 possessing moderate RIP1 kinase inhibitory activity and P-gp mediated efflux. The optimization of the core structure and the exploration of appropriate substituents utilizing SBDD approach led to the discovery of 22, a highly potent, orally available, and brain-penetrating RIP1 kinase inhibitor with excellent PK profiles. Compound 22 significantly suppressed necroptotic cell death both in mouse and human cells. Oral administration of 22 (10 mg/kg, bid) attenuated disease progression in the mouse experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS). Moreover, analysis of structure-kinetic relationship (SKR) for our novel chemical series was also discussed.

Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry

Although drug development typically focuses on binding thermodynamics, recent studies suggest that kinetic properties can strongly impact a drug candidate’s efficacy. Robust techniques for measuring inhibitor association and dissociation rates are therefore essential. To address this need, we have developed a pair of complementary isothermal titration calorimetry (ITC) techniques for measuring the kinetics of enzyme inhibition. The advantages of ITC over standard techniques include speed, generality, and versatility; ITC also measures the rate of catalysis directly, making it ideal for quantifying rapid, inhibitor-dependent changes in enzyme activity. We used our methods to study the reversible covalent and non-covalent inhibitors of prolyl oligopeptidase (POP). We extracted kinetics spanning three orders of magnitude, including those too rapid for standard methods, and measured sub-nM binding affinities below the typical ITC limit. These results shed light on the inhibition of POP and demonstrate the general utility of ITC-based enzyme inhibition kinetic measurements.

There is growing evidence that the kinetics of interactions between inhibitors and their targets can strongly impact therapeutic efficacy. Here the authors describe an isothermal titration calorimetry-based method that can rapidly quantify inhibition kinetics and measure sub-nM binding affinities.