Drug-Target Kinetics in Drug Discovery

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

High-throughput kinetics in drug discovery

The importance of a drug's kinetic profile and interplay of structure-kinetic activity with PK/PD has long been appreciated in drug discovery. However, technical challenges have often limited detailed kinetic characterization of compounds to the latter stages of projects. This review highlights the advances that have been made in recent years in techniques, instrumentation, and data analysis to increase the throughput of detailed kinetic and mechanistic characterization, enabling its application earlier in the drug discovery process.

Tyrosine kinase inhibitors in cancers: Treatment optimization - Part I

A multitude of TKI has been developed and approved targeting various oncogenetic alterations. While these have provided improvements in efficacy compared with conventional chemotherapies, resistance to targeted therapies occurs. Mutations in the kinase domain result in the inability of TKI to inactivate the protein kinase. Also, gene amplification, increased protein expression and downstream activation or bypassing of signalling pathways are commonly reported mechanisms of resistance. Improved understanding of mechanisms involved in TKI resistance has resulted in the development of new generations of targeted agents. In a race against time, the search for new, more potent and efficient drugs, and/or combinations of drugs, remains necessary as new resistance mechanisms to the latest generation of TKI emerge. This review examines the various generations of TKI approved to date and their common mechanisms of resistance, focusing on TKI targeting BCR-ABL, epidermal growth factor receptor, anaplastic lymphoma kinase and BRAF/MEK tyrosine kinases.

Imidazole[1,5-a]pyridine derivatives as EGFR tyrosine kinase inhibitors unraveled by umbrella sampling and steered molecular dynamics simulations

Although the use of the tyrosine kinase inhibitors (TKIs) has been proved that it can save live in a cancer treatment, the currently used drugs bring in many undesirable side-effects. Therefore, the search for new drugs and an evaluation of their efficiency are intensively carried out. Recently, a series of eighteen imidazole[1,5-a]pyridine derivatives were synthetized by us, and preliminary analyses pointed out their potential to be an important platform for pharmaceutical development owing to their promising actions as anticancer agents and enzyme (kinase, HIV-protease,…) inhibitors. In the present theoretical study, we further analyzed their efficiency in using a realistic scenario of computational drug design. Our protocol has been developed to not only observe the atomistic interaction between the EGFR protein and our 18 novel compounds using both umbrella sampling and steered molecular dynamics simulations, but also determine their absolute binding free energies. Calculated properties of the 18 novel compounds were in detail compared with those of two known drugs, erlotinib and osimertinib, currently used in cancer treatment. Inspiringly the simulation results promote three imidazole[1,5-a]pyridine derivatives as promising inhibitors into a further step of clinical trials.

Discovery and Preclinical Characterization of BIIB129, a Covalent, Selective, and Brain-Penetrant BTK Inhibitor for the Treatment of Multiple Sclerosis

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton's tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 (25), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity. BIIB129 (25) demonstrated efficacy in disease-relevant preclinical in vivo models of B cell proliferation in the CNS, exhibits a favorable safety profile suitable for clinical development as an immunomodulating therapy for MS, and has a low projected total human daily dose.

GS-441524-Diphosphate-Ribose Derivatives as Nanomolar Binders and Fluorescence Polarization Tracers for SARS-CoV-2 and Other Viral Macrodomains

Viral macrodomains that can bind to or hydrolyze protein adenosine diphosphate ribosylation (ADP-ribosylation) have emerged as promising targets for antiviral drug development. Many inhibitor development efforts have been directed against the severe acute respiratory syndrome coronavirus 2 macrodomain 1 (SARS-CoV-2 Mac1). However, potent inhibitors for viral macrodomains are still lacking, with the best inhibitors still in the micromolar range. Based on GS-441524, a remdesivir precursor, and our previous studies, we have designed and synthesized potent binders of SARS-CoV-2 Mac1 and other viral macrodomains including those of Middle East respiratory syndrome coronavirus (MERS-CoV), Venezuelan equine encephalitis virus (VEEV), and Chikungunya virus (CHIKV). We show that the 1'-CN group of GS-441524 promotes binding to all four viral macrodomains tested while capping the 1″-OH of GS-441524-diphosphate-ribose with a simple phenyl ring further contributes to binding. Incorporating these two structural features, the best binders show 20- to 6000-fold increases in binding affinity over ADP-ribose for SARS-CoV-2, MERS-CoV, VEEV, and CHIKV macrodomains. Moreover, building on these potent binders, we have developed two highly sensitive fluorescence polarization tracers that only require nanomolar proteins and can effectively resolve the binding affinities of nanomolar inhibitors. Our findings and probes described here will facilitate future development of more potent viral macrodomain inhibitors.

Quantitative Prediction of Dissociation Rates of PYK2 Ligands Using Umbrella Sampling and Milestoning

We used umbrella sampling and the milestoning simulation method to study the dissociation of multiple ligands from protein kinase PYK2. The activation barriers obtained from the potential of mean force of the umbrella sampling simulations correlated well with the experimental dissociation rates. Using the zero-temperature string method, we obtained optimized paths along the free-energy surfaces for milestoning simulations of three ligands with a similar structure. The milestoning simulations gave an absolute dissociation rate within 2 orders of magnitude of the experimental value for two ligands but at least 3 orders of magnitude too high for the third. Despite the similarity in their structures, the ligands took different pathways to exit from the binding site of PYK2, making contact with different sets of residues. In addition, the protein experienced different conformational changes for dissociation of the three ligands.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Data-oriented protein kinase drug discovery

The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts.

NB compounds are potent and efficacious FOXM1 inhibitors in high-grade serous ovarian cancer cells

BACKGROUND

Genetic studies implicate the oncogenic transcription factor Forkhead Box M1 (FOXM1) as a potential therapeutic target in high-grade serous ovarian cancer (HGSOC). We evaluated the activity of different FOXM1 inhibitors in HGSOC cell models.

RESULTS

We treated HGSOC and fallopian tube epithelial (FTE) cells with a panel of previously reported FOXM1 inhibitors. Based on drug potency, efficacy, and selectivity, determined through cell viability assays, we focused on two compounds, NB-73 and NB-115 (NB compounds), for further investigation. NB compounds potently and selectively inhibited FOXM1 with lesser effects on other FOX family members. NB compounds decreased FOXM1 expression via targeting the FOXM1 protein by promoting its proteasome-mediated degradation, and effectively suppressed FOXM1 gene targets at both the protein and mRNA level. At the cellular level, NB compounds promoted apoptotic cell death. Importantly, while inhibition of apoptosis using a pan-caspase inhibitor rescued HGSOC cells from NB compound-induced cell death, it did not rescue FOXM1 protein degradation, supporting that FOXM1 protein loss from NB compound treatment is specific and not a general consequence of cytotoxicity. Drug washout studies indicated that FOXM1 reduction was retained for at least 72 h post-treatment, suggesting that NB compounds exhibit long-lasting effects in HGSOC cells. NB compounds effectively suppressed both two-dimensional and three-dimensional HGSOC cell colony formation at sub-micromolar concentrations. Finally, NB compounds exhibited synergistic activity with carboplatin in HGSOC cells.

CONCLUSIONS

NB compounds are potent, selective, and efficacious inhibitors of FOXM1 in HGSOC cells and are worthy of further investigation as HGSOC therapeutics.

Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs-phenotypic screens and target-based drug discovery-and outline some of the limitations of each method. We then explore recent advances in genetic tools that aim to overcome some of these limitations. In particular, we highlight a novel metric to prioritize essential targets, termed vulnerability. Stratifying targets based on their vulnerability presents new opportunities for future target-based drug discovery campaigns.

Kinetic profiling of novel spirobenzo-oxazinepiperidinone derivatives as equilibrative nucleoside transporter 1 inhibitors

Evaluation of kinetic parameters of drug-target binding, kon, koff, and residence time (RT), in addition to the traditional in vitro parameter of affinity is receiving increasing attention in the early stages of drug discovery. Target binding kinetics emerges as a meaningful concept for the evaluation of a ligand's duration of action and more generally drug efficacy and safety. We report the biological evaluation of a novel series of spirobenzo-oxazinepiperidinone derivatives as inhibitors of the human equilibrative nucleoside transporter 1 (hENT1, SLC29A1). The compounds were evaluated in radioligand binding experiments, i.e., displacement, competition association, and washout assays, to evaluate their affinity and binding kinetic parameters. We also linked these pharmacological parameters to the compounds' chemical characteristics, and learned that separate moieties of the molecules governed target affinity and binding kinetics. Among the 29 compounds tested, 28 stood out with high affinity and a long residence time of 87 min. These findings reveal the importance of supplementing affinity data with binding kinetics at transport proteins such as hENT1.

Comprehensive Data-Driven Assessment of Non-Kinase Targets of Inhibitors of the Human Kinome

Protein kinases (PKs) are involved in many intracellular signal transduction pathways through phosphorylation cascades and have become intensely investigated pharmaceutical targets over the past two decades. Inhibition of PKs using small-molecular inhibitors is a premier strategy for the treatment of diseases in different therapeutic areas that are caused by uncontrolled PK-mediated phosphorylation and aberrant signaling. Most PK inhibitors (PKIs) are directed against the ATP cofactor binding site that is largely conserved across the human kinome comprising 518 wild-type PKs (and many mutant forms). Hence, these PKIs often have varying degrees of multi-PK activity (promiscuity) that is also influenced by factors such as single-site mutations in the cofactor binding region, compound binding kinetics, and residence times. The promiscuity of PKIs is often-but not always-critically important for therapeutic efficacy through polypharmacology. Various in vitro and in vivo studies have also indicated that PKIs have the potential of interacting with additional targets other than PKs, and different secondary cellular targets of individual PKIs have been identified on a case-by-case basis. Given the strong interest in PKs as drug targets, a wealth of PKIs from medicinal chemistry and their activity data from many assays and biological screens have become publicly available over the years. On the basis of these data, for the first time, we conducted a systematic search for non-PK targets of PKIs across the human kinome. Starting from a pool of more than 155,000 curated human PKIs, our large-scale analysis confirmed secondary targets from diverse protein classes for 447 PKIs on the basis of high-confidence activity data. These PKIs were active against 390 human PKs, covering all kinase groups of the kinome and 210 non-PK targets, which included other popular pharmaceutical targets as well as currently unclassified proteins. The target distribution and promiscuity of the 447 PKIs were determined, and different interaction profiles with PK and non-PK targets were identified. As a part of our study, the collection of PKIs with activity against non-PK targets and the associated information are made freely available.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Ligand-Based Competition Binding by Real-Time 19F NMR in Human Cells

The development of more effective drugs requires knowledge of their bioavailability and binding efficacy directly in the native cellular environment. In-cell nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for investigating ligand-target interactions directly in living cells. However, the target molecule may be NMR-invisible due to interactions with cellular components, while observing the ligand by 1H NMR is impractical due to the cellular background. Such limitations can be overcome by observing fluorinated ligands by 19F in-cell NMR as they bind to the intracellular target. Here we report a novel approach based on real-time in-cell 19F NMR that allows measuring ligand binding affinities in human cells by competition binding, using a fluorinated compound as a reference. The binding of a set of compounds toward Hsp90α was investigated. In principle, this approach could be applied to other pharmacologically relevant targets, thus aiding the design of more effective compounds in the early stages of drug development.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

Attempting Well-Tempered Funnel Metadynamics Simulations for the Evaluation of the Binding Kinetics of Methionine Aminopeptidase-II Inhibitors

Understanding the unbinding kinetics of protein-ligand complexes is considered a significant approach for the design of ligands with desired specificity and safety. In recent years, enhanced sampling methods have emerged as effective tools for studying the unbinding kinetics of protein-ligand complexes at the atomistic level. MetAP-II is a target for the treatment of cancer for which not a single effective drug is available yet. The identification of the dissociation rate of ligands from the complexes often serves as a better predictor for in vivo efficacy than the ligands' binding affinity. Here, funnel-based restraint well-tempered metadynamics simulations were applied to predict the residence time of two ligands bound to MetAP-II, along with the ligand association and dissociation mechanism involving the identification of the binding hotspot during ligand egress. The ligand-egressing route revealed by metadynamics simulations also correlated with the identified pathways from the CAVER analysis and by the enhanced sampling simulation using PLUMED. Ligand 1 formed a strong H-bond interaction with GLU364 estimating a higher residence time of 28.22 ± 5.29 ns in contrast to ligand 2 with a residence time of 19.05 ± 3.58 ns, which easily dissociated from the binding pocket of MetAP-II. The results obtained from the simulations were consistent to reveal ligand 1 being superior to ligand 2; however, the experimental data related to residence time were close for both ligands, and no kinetic data were available for ligand 2. The current study could be considered the first attempt to apply an enhanced sampling method for the evaluation of the binding kinetics and thermodynamics of two different classes of ligands to a binuclear metalloprotein.

Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.

How Robust Is the Ligand Binding Transition State?

For many drug targets, it has been shown that the kinetics of drug binding (e.g., on rate and off rate) is more predictive of drug efficacy than thermodynamic quantities alone. This motivates the development of predictive computational models that can be used to optimize compounds on the basis of their kinetics. The structural details underpinning these computational models are found not only in the bound state but also in the short-lived ligand binding transition states. Although transition states cannot be directly observed experimentally due to their extremely short lifetimes, recent successes have demonstrated that modeling the ligand binding transition state is possible with the help of enhanced sampling molecular dynamics methods. Previously, we generated unbinding paths for an inhibitor of soluble epoxide hydrolase (sEH) with a residence time of 11 min. Here, we computationally modeled unbinding events with the weighted ensemble method REVO (resampling of ensembles by variation optimization) for five additional inhibitors of sEH with residence times ranging from 14.25 to 31.75 min, with average prediction accuracy within an order of magnitude. The unbinding ensembles are analyzed in detail, focusing on features of the ligand binding transition state ensembles (TSEs). We find that ligands with similar bound poses can show significant differences in their ligand binding TSEs, in terms of their spatial distribution and protein-ligand interactions. However, we also find similarities across the TSEs when examining more general features such as ligand degrees of freedom. Together these findings show significant challenges for rational, kinetics-based drug design.

Ultrafast Detection of Monoamine Oxidase A in Live Cells and Clinical Glioma Tissues Using an Affinity Binding-Based Two-Photon Fluorogenic Probe

Abnormal expression of monoamine oxidase A (MAO-A) has been implicated in the development of human glioma, making MAO-A a promising target for therapy. Therefore, a rapid determination of MAO-A is critical for diagnosis. Through in silico screening of two-photon fluorophores, we discovered that a derivative of N,N-dimethyl-naphthalenamine (pre-mito) can effectively fit into the entrance of the MAO-A cavity. Substitutions on the N-pyridine not only further explore the MAO-A cavity, but also enable mitochondrial targeting ability. The aminopropyl substituted molecule, CD1, showed the fastest MAO-A detection (within 20 s), high MAO-A affinity and selectivity. It was also used for in situ imaging of MAO-A in living cells, enabling a comparison of the MAO-A content in human glioma and paracancerous tissues. Our results demonstrate that optimizing the affinity binding-based fluorogenic probes significantly improves their detection rate, providing a general approach for rapid detection probe design and optimization.

Encounter Complexes Between the N-terminal of Neurotensin with the Extracellular Loop 2 of the Neurotensin Receptor 1 Steer Neurotensin to the Orthosteric Binding Pocket

Neurotensin (NT) is a linear disordered peptide that activates two different class A GPCRs, neurotensin receptor 1 (NTS1) and NTS2. Resolved structures of the complex of the C-terminal fragment of NT, NT8-13, with NTS1 shows the peptide takes a well-defined structure in the bound state. However, the mechanisms underlying NT recognition of NTS1, and the conformational transition of NT upon binding NTS1 is an open question that if answered may aid discovery of highly selective drugs and reveal potential secondary binding sites on the surface of the receptor. Herein we investigated the interactions guiding NT to the orthosteric binding pocket of NTS1 by combining NMR experiments with kinetic analysis of the binding pathway using stopped-flow fluorescence and mutagenesis on both NT and NTS1. We show the presence of transient structures in the middle part of NT that kinetically regulate the binding of NT to NTS1. Moreover, our results indicate that the binding pathway of NT onto NTS1 is mediated via electrostatic interactions between the N-terminal region of NT with the extracellular loop 2 of NTS1. These interactions induce backbone conformational changes in neurotensin similar to the bound-state neurotensin, suggesting that the N-terminal region of NT and these interactions should be considered for development of selective drugs against NTS1.

Allosteric crosstalk in modular proteins: Function fine-tuning and drug design

Modular proteins are regulatory proteins that carry out more than one function. These proteins upregulate or downregulate a biochemical cascade to establish homeostasis in cells. To switch the function or alter the efficiency (based on cellular needs), these proteins require different facilitators that bind to a site different from the catalytic (active/orthosteric) site, aka 'allosteric site', and fine-tune their function. These facilitators (or effectors) are allosteric modulators. In this Review, we have discussed the allostery, characterized them based on their mechanisms, and discussed how allostery plays an important role in the activity modulation and function fine-tuning of proteins. Recently there is an emergence in the discovery of allosteric drugs. We have also emphasized the role, significance, and future of allostery in therapeutic applications.

Unraveling the Mechanism of Epichaperome Modulation by Zelavespib: Biochemical Insights on Target Occupancy and Extended Residence Time at the Site of Action

Drugs with a long residence time at their target sites are often more efficacious in disease treatment. The mechanism, however, behind prolonged retention at the site of action is often difficult to understand for non-covalent agents. In this context, we focus on epichaperome agents, such as zelavespib and icapamespib, which maintain target binding for days despite rapid plasma clearance, minimal retention in non-diseased tissues, and rapid metabolism. They have shown significant therapeutic value in cancer and neurodegenerative diseases by disassembling epichaperomes, which are assemblies of tightly bound chaperones and other factors that serve as scaffolding platforms to pathologically rewire protein-protein interactions. To investigate their impact on epichaperomes in vivo, we conducted pharmacokinetic and target occupancy measurements for zelavespib and monitored epichaperome assemblies biochemically in a mouse model. Our findings provide evidence of the intricate mechanism through which zelavespib modulates epichaperomes in vivo. Initially, zelavespib becomes trapped when epichaperomes bound, a mechanism that results in epichaperome disassembly, with no change in the expression level of epichaperome constituents. We propose that the initial trapping stage of epichaperomes is a main contributing factor to the extended on-target residence time observed for this agent in clinical settings. Zelavespib's residence time in tumors seems to be dictated by target disassembly kinetics rather than by frank drug-target unbinding kinetics. The off-rate of zelavespib from epichaperomes is, therefore, much slower than anticipated from the recorded tumor pharmacokinetic profile or as determined in vitro using diluted systems. This research sheds light on the underlying processes that make epichaperome agents effective in the treatment of certain diseases.

Development of Long-Acting Dipeptidyl Peptidase-4 Inhibitors: Structural Evolution and Long-Acting Determinants

Considerable effort has been made to achieve less frequent dosing in the development of DPP-4 inhibitors. Enthusiasm for long-acting DPP-4 inhibitors is based on the promise that such agents with less frequent dosing regimens are associated with improved patient adherence, but the rational design of long-acting DPP-4 inhibitors remains a major challenge. In this Perspective, the development of long-acting DPP-4 inhibitors is comprehensively summarized to highlight the evolution of initial lead compounds on the path toward developing long-acting DPP-4 inhibitors over nearly three decades. The determinants for long duration of action are then examined, including the nature of the target, potency, binding kinetics, crystal structures, selectivity, and preclinical and clinical pharmacokinetic and pharmacodynamic profiles. More importantly, several possible approaches for the rational design of long-acting drugs are discussed. We hope that this information will facilitate the design and development of safer and more effective long-acting DPP-4 inhibitors and other oral drugs.

The translational value of ligand-receptor binding kinetics in drug discovery

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50 /IC50 and KD /Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug-target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug-target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much-needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.

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.

Optimizing molecular potential models by imposing kinetic constraints with path reweighting

Empirical force fields employed in molecular dynamics simulations of complex systems are often optimized to reproduce experimentally determined structural and thermodynamic properties. In contrast, experimental knowledge about the interconversion rates between metastable states in such systems is hardly ever incorporated in a force field due to a lack of an efficient approach. Here, we introduce such a framework based on the relationship between dynamical observables, such as rate constants, and the underlying molecular model parameters using the statistical mechanics of trajectories. Given a prior ensemble of molecular dynamics trajectories produced with imperfect force field parameters, the approach allows for the optimal adaption of these parameters such that the imposed constraint of equally predicted and experimental rate constant is obeyed. To do so, the method combines the continuum path ensemble maximum caliber approach with path reweighting methods for stochastic dynamics. When multiple solutions are found, the method selects automatically the combination that corresponds to the smallest perturbation of the entire path ensemble, as required by the maximum entropy principle. To show the validity of the approach, we illustrate the method on simple test systems undergoing rare event dynamics. Next to simple 2D potentials, we explore particle models representing molecular isomerization reactions and protein-ligand unbinding. Besides optimal interaction parameters, the methodology gives physical insights into what parts of the model are most sensitive to the kinetics. We discuss the generality and broad implications of the methodology.

Sub-stoichiometric Modulation of Viral Targets-Potent Antiviral Agents That Exploit Target Vulnerability

The modulation of oligomeric viral targets at sub-stoichiometric ratios of drug to target has been advocated for its efficacy and potency, but there are only a limited number of documented examples. In this Viewpoint, we summarize the invention of the HIV-1 maturation inhibitor fipravirimat and discuss the emerging details around the mode of action of this class of drug that reflects inhibition of a protein composed of 1,300-1,600 monomers that interact in a cooperative fashion. Similarly, the HCV NS5A inhibitor daclatasvir has been shown to act in a highly sub-stoichiometric fashion, inhibiting viral replication at concentrations that are ∼23,500 lower than that of the protein target.

Expanding Chemical Probe Space: Quality Criteria for Covalent and Degrader Probes

Within druggable target space, new small-molecule modalities, particularly covalent inhibitors and targeted degraders, have expanded the repertoire of medicinal chemists. Molecules with such modes of action have a large potential not only as drugs but also as chemical probes. Criteria have previously been established to describe the potency, selectivity, and properties of small-molecule probes that are qualified to enable the interrogation and validation of drug targets. These definitions have been tailored to reversibly acting modulators but fall short in their applicability to other modalities. While initial guidelines have been proposed, we delineate here a full set of criteria for the characterization of covalent, irreversible inhibitors as well as heterobifunctional degraders ("proteolysis-targeting chimeras", or PROTACs) and molecular glue degraders. We propose modified potency and selectivity criteria compared to those for reversible inhibitors. We discuss their relevance and highlight examples of suitable probe and pathfinder compounds.

Incubation Time Influences Organic Anion Transporter 1 Kinetics and Renal Clearance Predictions

Accurate predictions of drug uptake transporter involvement in renal excretion of xenobiotics require determination of in vitro transport kinetic parameters under initial-rate conditions. The purpose of the present study was to determine how changing the incubation time from initial rate to steady state influences ligand interactions with the renal organic anion transporter 1 (OAT1), and the impact of the different experimental conditions on pharmacokinetic predictions. Transport studies were performed with Chinese hamster ovary cells expressing OAT1 (CHO-OAT1) and the Simcyp Simulator was used for physiological-based pharmacokinetic predictions. Maximal transport rate and intrinsic uptake clearance (CL_int) for PAH decreased with increasing incubation time. The CL_int values ranged 11-fold with incubation times spanning from 15 s (CL_int,15s, initial rate) to 45 min (CL_int,45min, steady state). The Michaelis constant (K_m) was also influenced by the incubation time with an apparent increase in the Km value at longer incubation times. Inhibition potency of five drugs against PAH transport was tested using incubation times of either 15 s or 10 min. There was no effect of time on inhibition potency for omeprazole or furosemide, whereas indomethacin was less potent, and probenecid (~2-fold) and telmisartan (~7-fold) more potent with the longer incubation time. Notably, the inhibitory effect of telmisartan was reversible, albeit slowly. A pharmacokinetic model was developed for PAH using the CL_int,15s value. The simulated plasma concentration-time profile, renal clearance, and cumulative urinary excretion-time profile of PAH agreed well with reported clinical data, and the PK parameters were sensitive to the time-associated CL_int value used in the model.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

A modified ELISA assay differentiates CCL20 locked dimers from wild-type monomers

A novel engineered CCL20 locked dimer (CCL20LD) is nearly identical to the naturally occurring chemokine CCL20 but blocks CCR6-mediated chemotaxis and offers a new approach to treat the diseases of psoriasis and psoriatic arthritis. Methods for quantifying CCL20LD serum levels are needed to assess pharmacokinetics parameters and evaluate drug delivery, metabolism, and toxicity. Existing ELISA kits fail to discriminate between CCL20LD and the natural chemokine, CCL20WT (the wild type monomer). Herein, we tested several available CCL20 monoclonal antibodies to be able to identify one clone that can be used both as a capture and a detection antibody (with biotin-labeling) to specifically detect CCL20LD with high specificity. After validation using recombinant proteins, the CCL20LD-selective ELISA was used to analyze blood samples from CCL20LD treated mice, demonstrating the utility of this novel assay for preclinical development of a biopharmaceutical lead compound for psoriatic disease.

An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs

Breast cancer treatment with PARP-1 inhibitors remains challenging due to emerging toxicities, drug resistance, and unaffordable costs of treatment options. How do we invent strategies to design better anti-cancer drugs? A part of the answer is in optimized compound properties, desirability functions, and modern computational drug design methods that drive selectivity and toxicity and have not been reviewed for PARP-1 inhibitors. Nonetheless, comparisons of these compound properties for PARP-1 inhibitors are not available in the literature. In this review, we analyze the physchem, PKPD space to identify inherent desirability functions characteristic of approved drugs that can be valuable for the design of better candidates. Recent literature utilizing ligand, structure-based drug design strategies and matched molecular pair analysis (MMPA) for the discovery of novel PARP-1 inhibitors are also reviewed. Thus, this perspective provides valuable insights into the medchem and multiparameter optimization of PARP-1 inhibitors that might be useful to other medicinal chemists.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

NanoB2 to monitor interactions of ligands with membrane proteins by combining nanobodies and NanoBRET

The therapeutic potential of ligands targeting disease-associated membrane proteins is predicted by ligand-receptor binding constants, which can be determined using NanoLuciferase (NanoLuc)-based bioluminescence resonance energy transfer (NanoBRET) methods. However, the broad applicability of these methods is hampered by the restricted availability of fluorescent probes. We describe the use of antibody fragments, like nanobodies, as universal building blocks for fluorescent probes for use in NanoBRET. Our nanobody-NanoBRET (NanoB2) workflow starts with the generation of NanoLuc-tagged receptors and fluorescent nanobodies, enabling homogeneous, real-time monitoring of nanobody-receptor binding. Moreover, NanoB2 facilitates the assessment of receptor binding of unlabeled ligands in competition binding experiments. The broad significance is illustrated by the successful application of NanoB2 to different drug targets (e.g., multiple G protein-coupled receptors [GPCRs] and a receptor tyrosine kinase [RTK]) at distinct therapeutically relevant binding sites (i.e., extracellular and intracellular).

Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models

Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Synthesis and Preclinical Evaluation of a Novel Fluorine-18-Labeled Tracer for Positron Emission Tomography Imaging of Bruton's Tyrosine Kinase

Bruton's tyrosine kinase (BTK) is a target for treating B-cell malignancies and autoimmune diseases. To aid in the discovery and development of BTK inhibitors and improve clinical diagnoses, we have developed a positron emission tomography (PET) radiotracer based on a selective BTK inhibitor, remibrutinib. [18F]PTBTK3 is an aromatic, 18F-labeled tracer that was synthesized in 3 steps with a 14.8 ± 2.4% decay-corrected radiochemical yield and ≥99% radiochemical purity. The cellular uptake of [18F]PTBTK3 was blocked up to 97% in JeKo-1 cells using remibrutinib or non-radioactive PTBTK3. [18F]PTBTK3 exhibited renal and hepatobiliary clearance in NOD SCID (non-obese diabetic/severe combined immunodeficiency) mice, and the tumor uptake of [18F]PTBTK3 in BTK-positive JeKo-1 xenografts (1.23 ± 0.30% ID/cc) was significantly greater at 60 min post injection compared to the tumor uptake in BTK-negative U87MG xenografts (0.41 ± 0.11% ID/cc). In the JeKo-1 xenografts, tumor uptake was blocked up to 62% by remibrutinib, indicating the BTK-dependent uptake of [18F]PTBTK3 in tumors.

Structure-Affinity and Structure-Kinetic Relationship Studies of Benzodiazepine Derivatives for the Development of Efficacious Vasopressin V2 Receptor Antagonists

Vasopressin V₂ receptors (V₂R) are a promising drug target for autosomal dominant polycystic kidney disease (ADPKD). As previous research demonstrated that the residence time of V₂R antagonists is critical to their efficacy in both ex vivo and in vivo models of ADPKD, we performed extensive structure-kinetic relationship (SKR) analyses on a series of benzodiazepine derivatives. We found that subtle structural modifications of the benzodiazepine derivatives dramatically changed their binding kinetics but not their affinity. Compound 18 exhibited a residence time of 77 min, which was 7.7-fold longer than that of the reference compound tolvaptan (TVP). Accordingly, compound 18 exhibited higher efficacy compared to TVP in an in vivo model of ADPKD. Overall, our study exemplifies a kinetics-directed medicinal chemistry effort for the development of efficacious V₂R antagonists. We envision that this strategy may also have general applicability in other therapeutic areas.

Dissociation kinetics of small-molecule inhibitors in Escherichia coli is coupled to physiological state of cells

Bioactive small-molecule inhibitors represent a treasure chest for future drugs. In vitro high-throughput screening is a common approach to identify the small-molecule inhibitors that bind tightly to purified targets. Here, we investigate the inhibitor-target binding/unbinding kinetics in E. coli cells using a benzimidazole-derivative DNA inhibitor as a model system. We find that its unbinding rate is not constant but depends on cell growth rate. This dependence is mediated by the cellular activity, forming a feedback loop with the inhibitor's activity. In accordance with this feedback, we find cell-to-cell heterogeneity in inhibitor-target interaction, leading to co-existence of two distinct subpopulations: actively growing cells that dissociate the inhibitors from the targets and non-growing cells that do not. We find similar heterogeneity for other clinical DNA inhibitors. Our studies reveal a mechanism that couples inhibitor-target kinetics to cell physiology and demonstrate the significant effect of this coupling on drug efficacy.

Rapid screening and in vivo target occupancy quantitative evaluation of xanthine oxidase inhibitors based on drug-target binding kinetics research strategy: A case study of Chrysanthemum morifolium Ramat

Chrysanthemum morifolium Ramat. is a kind of food and drug dual-use traditional Chinese medicine possessing multiple pharmacological and biochemical benefits. In our study, a rapid and high-throughput method based on Surface plasmon resonance (SPR) biosensor technology was developed and verified for screening potential xanthine oxidase (XOD) inhibitors exemplarily in the Chrysanthemum morifolium Ramat. Coupled with ultra-high performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS), 14 XOD-binders were identified. In the SPR-based biosensor and molecular docking analysis, most compounds exhibited a strong affinity and binding kinetic property (association rate constant, K_on and dissociation rate constant, K_off) for XOD and could be regarded as potential inhibitors. More importantly, to further accurately assess target occupancy of candidate compounds in vivo, a mathematical model was established and verified involving three crucial intrinsic kinetic processes (Pharmacokinetics, Binding kinetic and Target kinetic). Overall, the proposed screening and assessment strategy could be proved an effective theoretical basis for further pharmacodynamic evaluation.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

Transferring chemical and energetic knowledge between molecular systems with machine learning

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

Cosolvent Dimethyl Sulfoxide Influences Protein-Ligand Binding Kinetics via Solvent Viscosity Effects: Revealing the Success Rate of Complex Formation Following Diffusive Protein-Ligand Encounter

Protein-ligand-exchange kinetics determines the duration of biochemical signals and consequently plays an important role in drug design. Binding studies commonly require solubilization of designed ligands in solvents such as dimethyl sulfoxide (DMSO), resulting in residual amounts of DMSO following titration of solubilized ligands into aqueous protein samples. Therefore, it is critical to establish whether DMSO influences protein-ligand binding. Here, we address the general and indirect effect of DMSO on protein-ligand binding caused by solvent viscosity, which is strongly dependent on the relative concentrations of DMSO and water. As a model system, we studied the binding of a drug-like ligand to the carbohydrate recognition domain of galectin-3 in the presence of variable amounts of DMSO. We used isothermal titration calorimetry to characterize binding thermodynamics and ¹⁵N NMR relaxation to monitor kinetics. The binding enthalpy is not affected, but we observe a subtle trend of increasingly unfavorable entropy of binding, and consequently decreased affinity, with increasing DMSO concentration. The increasing concentration of DMSO results in a reduced association rate of binding, while the dissociation rate is less affected. The observed association rate is inversely proportional to the viscosity of the DMSO-water mixture, as expected from theory, but significantly reduced from the diffusion-controlled limit. By comparing the viscosity dependence of the observed association rate with that of the theoretical diffusion-controlled association rate, we estimate the success rate of productive complex formation following an initial encounter of proteins and ligands, showing that only one out of several hundred binding "attempts" are successful.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Heterobivalent Inhibitors of Acetyl-CoA Carboxylase: Drug Target Residence Time and Time-Dependent Antibacterial Activity

The relationship between drug-target residence time and the post-antibiotic effect (PAE) provides insights into target vulnerability. To probe the vulnerability of bacterial acetyl-CoA carboxylase (ACC), a series of heterobivalent inhibitors were synthesized based on pyridopyrimidine 1 and moiramide B (3) which bind to the biotin carboxylase and carboxyltransferase ACC active sites, respectively. The heterobivalent compound 17, which has a linker of 50 Å, was a tight binding inhibitor of Escherichia coli ACC (Kiapp 0.2 nM) and could be displaced from ACC by a combination of both 1 and 3 but not just by 1. In agreement with the prolonged occupancy of ACC resulting from forced proximity binding, the heterobivalent inhibitors produced a PAE in E. coli of 1-4 h in contrast to 1 and 3 in combination or alone, indicating that ACC is a vulnerable target and highlighting the utility of kinetic, time-dependent effects in the drug mechanism of action.

Dimers of D76N-β2-microglobulin display potent antiamyloid aggregation activity

Self-association of WT β_2-microglobulin (WT-β₂m) into amyloid fibrils is associated with the disorder dialysis related amyloidosis. In the familial variant D76N-β₂m, the single amino acid substitution enhances the aggregation propensity of the protein dramatically and gives rise to a disorder that is independent of renal dysfunction. Numerous biophysical and structural studies on WT- and D76N-β₂m have been performed in order to better understand the structure and dynamics of the native proteins and their different potentials to aggregate into amyloid. However, the structural properties of transient D76N-β₂m oligomers and their role(s) in assembly remained uncharted. Here, we have utilized NMR methods, combined with photo-induced crosslinking, to detect, trap, and structurally characterize transient dimers of D76N-β₂m. We show that the crosslinked D76N-β₂m dimers have different structures from those previously characterized for the on-pathway dimers of ΔN6-β₂m and are unable to assemble into amyloid. Instead, the crosslinked D76N-β₂m dimers are potent inhibitors of amyloid formation, preventing primary nucleation and elongation/secondary nucleation when added in substoichiometric amounts with D76N-β₂m monomers. The results highlight the specificity of early protein-protein interactions in amyloid formation and show how mapping these interfaces can inform new strategies to inhibit amyloid assembly.

The Discovery of GSK3640254, a Next-Generation Inhibitor of HIV-1 Maturation

GSK3640254 is an HIV-1 maturation inhibitor (MI) that exhibits significantly improved antiviral activity toward a range of clinically relevant polymorphic variants with reduced sensitivity toward the second-generation MI GSK3532795 (BMS-955176). The key structural difference between GSK3640254 and its predecessor is the replacement of the para-substituted benzoic acid moiety attached at the C-3 position of the triterpenoid core with a cyclohex-3-ene-1-carboxylic acid substituted with a CH2F moiety at the carbon atom α- to the pharmacophoric carboxylic acid. This structural element provided a new vector with which to explore structure-activity relationships (SARs) and led to compounds with improved polymorphic coverage while preserving pharmacokinetic (PK) properties. The approach to the design of GSK3640254, the development of a synthetic route and its preclinical profile are discussed. GSK3640254 is currently in phase IIb clinical trials after demonstrating a dose-related reduction in HIV-1 viral load over 7-10 days of dosing to HIV-1-infected subjects.