Drug-Target Kinetics in Drug Discovery

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.

Structure-Kinetic Relationship Studies for the Development of Long Residence Time LpxC Inhibitors

UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) is a promising drug target in Gram-negative bacteria. Previously, we described a correlation between the residence time of inhibitors on Pseudomonas aeruginosa LpxC (paLpxC) and the post-antibiotic effect (PAE) caused by the inhibitors on the growth of P. aeruginosa. Given that drugs with prolonged activity following compound removal may have advantages in dosing regimens, we have explored the structure-kinetic relationship for paLpxC inhibition by analogues of the pyridone methylsulfone PF5081090 (1) originally developed by Pfizer. Several analogues have longer residence times on paLpxC than 1 (41 min) including PT913, which has a residence time of 124 min. PT913 also has a PAE of 4 h, extending the original correlation observed between residence time and PAE. Collectively, the studies provide a platform for the rational modulation of paLpxC inhibitor residence time and the potential development of antibacterial agents that cause prolonged suppression of bacterial growth.

Bioresponsive Polymers for Nanomedicine-Expectations and Reality!

Bioresponsive polymers in nanomedicine have been widely perceived to selectively activate the therapeutic function of nanomedicine at diseased or pathological sites, while sparing their healthy counterparts. This idea can be described as an advanced version of Paul Ehrlich's magic bullet concept. From that perspective, the inherent anomalies or malfunction of the pathological sites are generally targeted to allow the selective activation or sensory function of nanomedicine. Nonetheless, while the primary goals and expectations in developing bioresponsive polymers are to elicit exclusive selectivity of therapeutic action at diseased sites, this remains difficult to achieve in practice. Numerous research efforts have been undertaken, and are ongoing, to tackle this fine-tuning. This review provides a brief introduction to key stimuli with biological relevance commonly featured in the design of bioresponsive polymers, which serves as a platform for critical discussion, and identifies the gap between expectations and current reality.

An FDA-Approved Antifungal, Ketoconazole, and Its Novel Derivative Suppress tGLI1-Mediated Breast Cancer Brain Metastasis by Inhibiting the DNA-Binding Activity of Brain Metastasis-Promoting Transcription Factor tGLI1

The goal of this study is to identify pharmacological inhibitors that target a recently identified novel mediator of breast cancer brain metastasis (BCBM), truncated glioma-associated oncogene homolog 1 (tGLI1). Inhibitors of tGLI1 are not yet available. To identify compounds that selectively kill tGLI1-expressing breast cancer, we screened 1527 compounds using two sets of isogenic breast cancer and brain-tropic breast cancer cell lines engineered to stably express the control, GLI1, or tGLI1 vector, and identified the FDA-approved antifungal ketoconazole (KCZ) to selectively target tGLI1-positive breast cancer cells and breast cancer stem cells, but not tGLI1-negative breast cancer and normal cells. KCZ's effects are dependent on tGLI1. Two experimental mouse metastasis studies have demonstrated that systemic KCZ administration prevented the preferential brain metastasis of tGLI1-positive breast cancer and suppressed the progression of established tGLI1-positive BCBM without liver toxicities. We further developed six KCZ derivatives, two of which (KCZ-5 and KCZ-7) retained tGLI1-selectivity in vitro. KCZ-7 exhibited higher blood-brain barrier penetration than KCZ/KCZ-5 and more effectively reduced the BCBM frequency. In contrast, itraconazole, another FDA-approved antifungal, failed to suppress BCBM. The mechanistic studies suggest that KCZ and KCZ-7 inhibit tGLI1's ability to bind to DNA, activate its target stemness genes Nanog and OCT4, and promote tumor proliferation and angiogenesis. Our study establishes the rationale for using KCZ and KCZ-7 for treating and preventing BCBM and identifies their mechanism of action.

Pharmacodynamic model of slow reversible binding and its applications in pharmacokinetic/pharmacodynamic modeling: review and tutorial

Therapeutic responses of most drugs are initiated by the rate and degree of binding to their receptors or targets. The law of mass action describes the rate of drug-receptor complex association (k_on) and dissociation (k_off) where the ratio k_off/k_on is the equilibrium dissociation constant (K_d). Drugs with slow reversible binding (SRB) often demonstrate delayed onset and prolonged pharmacodynamic effects. This report reviews evidence for drugs with SRB features, describes previous pharmacokinetic/pharmacodynamic (PK/PD) modeling efforts of several such drugs, provides a tutorial on the mathematics and properties of SRB models, demonstrates applications of SRB models to additional compounds, and compares PK/PD fittings of SRB with other mechanistic models. We identified and summarized 52 drugs with in vitro-confirmed SRB from a PubMed literature search. Simulations with a SRB model and observed PK/PD profiles showed delayed and prolonged responses and that increasing doses/k_on or decreasing k_off led to greater expected maximum effects and a longer duration of effects. Recession slopes for return of responses to baseline after single doses were nearly linear with an inflection point that approaches a limiting value at larger doses. The SRB model newly captured literature data for the antihypertensive effects of candesartan and antiallergic effects of noberastine. Their PD profiles could also be fitted with indirect response and biophase models with minimal differences. The applicability of SRB models is probably commonplace, but underappreciated, owing to the need for in vitro confirmation of binding kinetics and the similarity of PK/PD profiles to models with other mechanistic determinants.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity-relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity-activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Low-Molecular Pyrazine-Based DNA Binders: Physicochemical and Antimicrobial Properties

Pyrazine and its derivatives are a large group of compounds that exhibit broad biological activity, the changes of which can be easily detected by a substituent effect or a change in the functional group. The present studies combined theoretical research with the density functional theory (DFT) approach (B3LYP/6-311+G**) and experimental (potentiometric and spectrophotometric) analysis for a thorough understanding of the structure of chlorohydrazinopyrazine, its physicochemical and cytotoxic properties, and the site and nature of interaction with DNA. The obtained results indicated that 2-chloro-3-hydrazinopyrazine (2Cl3HP) displayed the highest affinity to DNA. Cytotoxicity studies revealed that the compound did not exhibit toxicity toward human dermal keratinocytes, which supported the potential application of 2Cl3HP in clinical use. The study also attempted to establish the possible equilibria occurring in the aqueous solution and, using both theoretical and experimental methods, clearly showed the hydrophilic nature of the compound. The experimental and theoretical results of the study confirmed the quality of the compound, as well as the appropriateness of the selected set of methods for similar research.

Kinetic Modeling of Time-Dependent Enzyme Inhibition by Pre-Steady-State Analysis of Progress Curves: The Case Study of the Anti-Alzheimer's Drug Galantamine

The Michaelis–Menten model of enzyme kinetic assumes the free ligand approximation, the steady-state approximation and the rapid equilibrium approximation. Analytical methods to model slow-binding inhibitors by the analysis of initial velocities have been developed but, due to their inherent complexity, they are seldom employed. In order to circumvent the complications that arise from the violation of the rapid equilibrium assumption, inhibition is commonly evaluated by pre-incubating the enzyme and the inhibitors so that, even for slow inhibitors, the binding equilibrium is established before the reaction is started. Here, we show that for long drug-target residence time inhibitors, the conventional analysis of initial velocities by the linear regression of double-reciprocal plots fails to provide a correct description of the inhibition mechanism. As a case study, the inhibition of acetylcholinesterase by galantamine, a drug approved for the symptomatic treatment of Alzheimer’s disease, is reported. For over 50 years, analysis based on the conventional steady-state model has overlooked the time-dependent nature of galantamine inhibition, leading to an erroneous assessment of the drug potency and, hence, to discrepancies between biochemical data and the pharmacological evidence. Re-examination of acetylcholinesterase inhibition by pre-steady state analysis of the reaction progress curves showed that the potency of galantamine has indeed been underestimated by a factor of ~100.

Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety

Drug optimization, which improves drug potency/specificity by structure‒activity relationship (SAR) and drug-like properties, is rigorously performed to select drug candidates for clinical trials. However, the current drug optimization may overlook the structure‒tissue exposure/selectivity-relationship (STR) in disease-targeted tissues vs. normal tissues, which may mislead the drug candidate selection and impact the balance of clinical efficacy/toxicity. In this study, we investigated the STR in correlation with observed clinical efficacy/toxicity using seven selective estrogen receptor modulators (SERMs) that have similar structures, same molecular target, and similar/different pharmacokinetics. The results showed that drug's plasma exposure was not correlated with drug's exposures in the target tissues (tumor, fat pad, bone, uterus), while tissue exposure/selectivity of SERMs was correlated with clinical efficacy/safety. Slight structure modifications of four SERMs did not change drug's plasma exposure but altered drug's tissue exposure/selectivity. Seven SERMs with high protein binding showed higher accumulation in tumors compared to surrounding normal tissues, which is likely due to tumor EPR effect of protein-bound drugs. These suggest that STR alters drug's tissue exposure/selectivity in disease-targeted tissues vs. normal tissues impacting clinical efficacy/toxicity. Drug optimization needs to balance the SAR and STR in selecting drug candidate for clinical trial to improve success of clinical drug development.

A Scintillation Proximity Assay for Real-Time Kinetic Analysis of Chemokine-Chemokine Receptor Interactions

Chemokine receptors are extensively involved in a broad range of physiological and pathological processes, making them attractive drug targets. However, despite considerable efforts, there are very few approved drugs targeting this class of seven transmembrane domain receptors to date. In recent years, the importance of including binding kinetics in drug discovery campaigns was emphasized. Therefore, kinetic insight into chemokine-chemokine receptor interactions could help to address this issue. Moreover, it could additionally deepen our understanding of the selectivity and promiscuity of the chemokine-chemokine receptor network. Here, we describe the application, optimization and validation of a homogenous Scintillation Proximity Assay (SPA) for real-time kinetic profiling of chemokine-chemokine receptor interactions on the example of ACKR3 and CXCL12. The principle of the SPA is the detection of radioligand binding to receptors reconstituted into nanodiscs by scintillation light. No receptor modifications are required. The nanodiscs provide a native-like environment for receptors and allow for full control over bilayer composition and size. The continuous assay format enables the monitoring of binding reactions in real-time, and directly accounts for non-specific binding and potential artefacts. Minor adaptations additionally facilitate the determination of equilibrium binding metrics, making the assay a versatile tool for the study of receptor-ligand interactions.

Kinetic profiling and functional characterization of 8-phenylxanthine derivatives as A2B adenosine receptor antagonists

A_2B adenosine receptor (A_2BAR) antagonists have therapeutic potential in inflammation-related diseases such as asthma, chronic obstructive pulmonary disease and cancer. However, no drug is currently clinically approved, creating a demand for research on novel antagonists. Over the last decade, the study of target binding kinetics, along with affinity and potency, has been proven valuable in early drug discovery stages, as it is associated with improved in vivo drug efficacy and safety. In this study, we report the synthesis and biological evaluation of a series of xanthine derivatives as A_2BAR antagonists, including an isothiocyanate derivative designed to bind covalently to the receptor. All 28 final compounds were assessed in radioligand binding experiments, to evaluate their affinity and for those qualifying, kinetic binding parameters. Both structure-affinity and structure-kinetic relationships were derived, providing a clear relationship between affinity and dissociation rate constants. Two structurally similar compounds, 17 and 18, were further evaluated in a label-free assay due to their divergent kinetic profiles. An extended cellular response was associated with long A_2BAR residence times. This link between a ligand's A_2BAR residence time and its functional effect highlights the importance of binding kinetics as a selection parameter in the early stages of drug discovery.

Long Residence Time at the Vasopressin V2 Receptor Translates into Superior Inhibitory Effects in Ex Vivo and In Vivo Models of Autosomal Dominant Polycystic Kidney Disease

Prevailing strategies directing early-phase drug discovery heavily rely on equilibrium-based metrics such as affinity, which overlooks the kinetic process of a drug molecule interacting with its target. Herein, we developed a number of vasopressin V₂ receptor (V₂R) antagonists with divergent binding affinities and kinetics for autosomal dominant polycystic kidney disease (ADPKD). Surprisingly, the residence time of the V₂R antagonists, but not their affinity, was correlated with the efficacy in both ex vivo and in vivo models of ADPKD. We envision that the kinetics-directed drug candidate selection and development may have general applicability for ADPKD and other therapeutic areas as well.

International Union of Basic and Clinical Pharmacology. CXII: Adenosine Receptors: A Further Update

Our previous International Union of Basic and Clinical Pharmacology report on the nomenclature and classification of adenosine receptors (2011) contained a number of emerging developments with respect to this G protein-coupled receptor subfamily, including protein structure, protein oligomerization, protein diversity, and allosteric modulation by small molecules. Since then, a wealth of new data and results has been added, allowing us to explore novel concepts such as target binding kinetics and biased signaling of adenosine receptors, to examine a multitude of receptor structures and novel ligands, to gauge new pharmacology, and to evaluate clinical trials with adenosine receptor ligands. This review should therefore be considered a further update of our previous reports from 2001 and 2011. SIGNIFICANCE STATEMENT: Adenosine receptors (ARs) are of continuing interest for future treatment of chronic and acute disease conditions, including inflammatory diseases, neurodegenerative afflictions, and cancer. The design of AR agonists ("biased" or not) and antagonists is largely structure based now, thanks to the tremendous progress in AR structural biology. The A2A- and A2BAR appear to modulate the immune response in tumor biology. Many clinical trials for this indication are ongoing, whereas an A2AAR antagonist (istradefylline) has been approved as an anti-Parkinson agent.

Triggered azobenzene-based prodrugs and drug delivery systems

Azobenzene-based molecules show unique trans-cis isomerization upon ultraviolet light irradiation, which induce the change of polarity, crystallinity, stability, and binding affinity with pharmacological target. Moreover, azobenzene is the substrate of azoreductase that is often overexpressed in many pathological sites, e.g. hypoxic solid tumor. Therefore, azobenzene can be a multifunctional molecule in material science, pharmaceutical science and biomedicine because of its sensitivity to light, hypoxia and certain enzymes, hence showing potential application in site-specific smart therapy. Herein we focus on the employment of azobenzene and its derivatives for engineering triggered prodrug and drug delivery systems, and provide an overview of photoswitchable azo-based prodrugs, the associated problems regarding ultraviolet light and reversible isomerization, as well as the potential solutions. We also present the advance of azo-bearing delivery vehicles wherein azobenzene act as the linker, capping agent, and building block, and discuss the corresponding mechanisms for controlled cargo release, endocytosis enhancement and sensitization of free radical cancer therapy.

Molecular Simulations and Drug Discovery of Adenosine Receptors

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.

Ligands with different dimeric configurations potently activate the EphA2 receptor and reveal its potential for biased signaling

The EphA2 receptor tyrosine kinase activates signaling pathways with different, and sometimes opposite, effects in cancer and other pathologies. Thus, highly specific and potent biased ligands that differentially control EphA2 signaling responses could be therapeutically valuable. Here, we use EphA2-specific monomeric peptides to engineer dimeric ligands with three different geometric configurations to combine a potential ability to differentially modulate EphA2 signaling responses with the high potency and prolonged receptor residence time characteristic of dimeric ligands. The different dimeric peptides readily induce EphA2 clustering, autophosphorylation and signaling, the best with sub-nanomolar potency. Yet, there are differences in two EphA2 signaling responses induced by peptides with different configurations, which exhibit distinct potency and efficacy. The peptides bias signaling when compared with the ephrinA1-Fc ligand and do so via different mechanisms. These findings provide insights into Eph receptor signaling, and proof-of-principle that different Eph signaling responses can be distinctly modulated.

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.

Nanomedicine for brain cancer

CNS tumors remain among the deadliest forms of cancer, resisting conventional and new treatment approaches, with mortality rates staying practically unchanged over the past 30 years. One of the primary hurdles for treating these cancers is delivering drugs to the brain tumor site in therapeutic concentration, evading the blood-brain (tumor) barrier (BBB/BBTB). Supramolecular nanomedicines (NMs) are increasingly demonstrating noteworthy prospects for addressing these challenges utilizing their unique characteristics, such as improving the bioavailability of the payloadsviacontrolled pharmacokinetics and pharmacodynamics, BBB/BBTB crossing functions, superior distribution in the brain tumor site, and tumor-specific drug activation profiles. Here, we review NM-based brain tumor targeting approaches to demonstrate their applicability and translation potential from different perspectives. To this end, we provide a general overview of brain tumor and their treatments, the incidence of the BBB and BBTB, and their role on NM targeting, as well as the potential of NMs for promoting superior therapeutic effects. Additionally, we discuss critical issues of NMs and their clinical trials, aiming to bolster the potential clinical applications of NMs in treating these life-threatening diseases.

Design of SARS-CoV-2 PLpro Inhibitors for COVID-19 Antiviral Therapy Leveraging Binding Cooperativity

Antiviral agents that complement vaccination are urgently needed to end the COVID-19 pandemic. The SARS-CoV-2 papain-like protease (PLpro), one of only two essential cysteine proteases that regulate viral replication, also dysregulates host immune sensing by binding and deubiquitination of host protein substrates. PLpro is a promising therapeutic target, albeit challenging owing to featureless P1 and P2 sites recognizing glycine. To overcome this challenge, we leveraged the cooperativity of multiple shallow binding sites on the PLpro surface, yielding novel 2-phenylthiophenes with nanomolar inhibitory potency. New cocrystal structures confirmed that ligand binding induces new interactions with PLpro: by closing of the BL2 loop of PLpro forming a novel "BL2 groove" and by mimicking the binding interaction of ubiquitin with Glu167 of PLpro. Together, this binding cooperativity translates to the most potent PLpro inhibitors reported to date, with slow off-rates, improved binding affinities, and low micromolar antiviral potency in SARS-CoV-2-infected human cells.

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.

Probing altered enzyme activity in the biochemical characterization of cancer

Enzymes have evolved to catalyze their precise reactions at the necessary rates, locations, and time to facilitate our development, to respond to a variety of insults and challenges, and to maintain a healthy, balanced state. Enzymes achieve this extraordinary feat through their unique kinetic parameters, myriad regulatory strategies, and their sensitivity to their surroundings, including substrate concentration and pH. The Cancer Genome Atlas (TCGA) highlights the extraordinary number of ways in which the finely tuned activities of enzymes can be disrupted, contributing to cancer development and progression often due to somatic and/or inherited genetic alterations. Rather than being limited to the domain of enzymologists, kinetic constants such as k_cat, K_m, and k_cat/K_m are highly informative parameters that can impact a cancer patient in tangible ways—these parameters can be used to sort tumor driver mutations from passenger mutations, to establish the pathways that cancer cells rely on to drive patients’ tumors, to evaluate the selectivity and efficacy of anti-cancer drugs, to identify mechanisms of resistance to treatment, and more. In this review, we will discuss how changes in enzyme activity, primarily through somatic mutation, can lead to altered kinetic parameters, new activities, or changes in conformation and oligomerization. We will also address how changes in the tumor microenvironment can affect enzymatic activity, and briefly describe how enzymology, when combined with additional powerful tools, and can provide us with tremendous insight into the chemical and molecular mechanisms of cancer.

Screening approaches and therapeutic targets: The two driving wheels of tuberculosis drug discovery

Tuberculosis (TB) is an infectious disease, infecting a quarter of world's population. Drug resistant TB further exacerbates the grim scenario of the drying TB drug discovery pipeline. The limited arsenal to fight TB presses the need for thorough efforts for identifying promising hits to combat the disease. The review highlights the efforts in the field of tuberculosis drug discovery, with an emphasis on massive drug screening campaigns for identifying novel hits against Mtb in both industry and academia. As an intracellular pathogen, mycobacteria reside in a complicated intracellular environment with multiple factors at play. Here, we outline various strategies employed in an effort to mimic the intracellular milieu for bringing the screening models closer to the actual settings. The review also focuses on the novel targets and pathways that could aid in target-based drug discovery in TB. The recent high throughput screening efforts resulting in the identification of potent hits against Mtb has been summarized in this article. There is a pressing need for effective screening strategies and approaches employing innovative tools and recent technologies; including nanotechnology, gene-editing tools such as CRISPR-cas system, host-directed bacterial killing and high content screening to augment the TB drug discovery pipeline with safer and shorter drug regimens.

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

In vitro antitumor activity of dialkylamine-1,4-naphthoquinones toward human glioblastoma multiforme cells

In this study, we evaluated the in vitro antitumor activity of dialkylamino-1,4-naphthoquinones (1a–n) toward human glioblastoma multiforme cells (GBM02).

Molecular Characteristics of Repotrectinib That Enable Potent Inhibition of TRK Fusion Proteins and Resistant Mutations

NTRK chromosomal rearrangements yield oncogenic TRK fusion proteins that are sensitive to TRK inhibitors (larotrectinib and entrectinib) but often mutate, limiting the durability of response for NTRK ⁺ patients. Next-generation inhibitors with compact macrocyclic structures (repotrectinib and selitrectinib) were designed to avoid resistance mutations. Head-to-head potency comparisons of TRK inhibitors and molecular characterization of binding interactions are incomplete, obscuring a detailed understanding of how molecular characteristics translate to potency. Larotrectinib, entrectinib, selitrectinib, and repotrectinib were characterized using cellular models of wild-type TRKA/B/C fusions and resistance mutant variants with a subset evaluated in xenograft tumor models. Crystal structures were determined for repotrectinib bound to TRKA (wild-type, solvent-front mutant). TKI-naïve and pretreated case studies are presented. Repotrectinib was the most potent inhibitor of wild-type TRKA/B/C fusions and was more potent than selitrectinib against all tested resistance mutations, underscoring the importance of distinct features of the macrocycle structures. Cocrystal structures of repotrectinib with wild-type TRKA and the TRKA^G595R SFM variant elucidated how differences in macrocyclic inhibitor structure, binding orientation, and conformational flexibility affect potency and mutant selectivity. The SFM crystal structure revealed an unexpected intramolecular arginine sidechain interaction. Repotrectinib caused tumor regression in LMNA-NTRK1 xenograft models harboring GKM, SFM, xDFG, and GKM + SFM compound mutations. Durable responses were observed in TKI-naïve and -pretreated patients with NTRK ⁺ cancers treated with repotrectinib (NCT03093116). This comprehensive analysis of first- and second-generation TRK inhibitors informs the clinical utility, structural determinants of inhibitor potency, and design of new generations of macrocyclic inhibitors.

The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.

G Protein-Coupled Receptor-Ligand Dissociation Rates and Mechanisms from τRAMD Simulations

There is a growing appreciation of the importance of drug-target binding kinetics for lead optimization. For G protein-coupled receptors (GPCRs), which mediate signaling over a wide range of time scales, the drug dissociation rate is often a better predictor of in vivo efficacy than binding affinity, although it is more challenging to compute. Here, we assess the ability of the τ-Random Acceleration Molecular Dynamics (τRAMD) approach to reproduce relative residence times and reveal dissociation mechanisms and the effects of allosteric modulation for two important membrane-embedded drug targets: the β2-adrenergic receptor and the muscarinic acetylcholine receptor M2. The dissociation mechanisms observed in the relatively short RAMD simulations (in which molecular dynamics (MD) simulations are performed using an additional force with an adaptively assigned random orientation applied to the ligand) are in general agreement with much more computationally intensive conventional MD and metadynamics simulations. Remarkably, although decreasing the magnitude of the random force generally reduces the number of egress routes observed, the ranking of ligands by dissociation rate is hardly affected and agrees well with experiment. The simulations also reproduce changes in residence time due to allosteric modulation and reveal associated changes in ligand dissociation pathways.

Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics

The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.

Application of modified Michaelis - Menten equations for determination of enzyme inducing and inhibiting drugs

BACKGROUND

Pharmacokinetics (PK) is the process of absorption, distribution, metabolism and elimination (ADME) of drugs. Some drugs undergo zero-order kinetics (ethyl alcohol), first order kinetics (piroxicam) and mixed order kinetics (ascorbic acid). Drugs that undergo Michaelis-Menten metabolism are characterized by either increased or decreased metabolism constant (Km) and maximum velocity (Vmax) of enzyme reaction. Hence literatures were searched with a view to translating in vitro-in vivo enzyme kinetics to pharmacokinetic/pharmacodynamic parameters for determination of enzyme inducing and inhibiting drugs, in order to achieve optimal clinical efficacy and safety.

METHODS

A narrative review of retrospective secondary data on drugs, their metabolites, Vmax and Km, generated in the laboratory and clinical environments was adopted, using inclusion and exclusion criteria. Key word search strategy was applied, to assess databases of published articles on enzyme inducing and inhibiting drugs, that obey Michaelis-Menten kinetics. In vitro and in vivo kinetic parameters, such as concentration of substrate, rate of endogenous substrate production, cellular metabolic rate, initial velocity of metabolism, intrinsic clearance, percent saturation and unsaturation of the enzyme substrate, were calculated using original and modified formulas. Years and numbers of searched publications, types of equations and their applications were recorded.

RESULTS

A total of fifty-six formulas both established and modified were applied in the present study. Findings have shown that theophylline, voriconazole, phenytoin, thiopental, fluorouracil, thyamine and thymidine are enzyme inducers whereas, mibefradil, metronidazole, isoniazid and puromicin are enzyme inhibitors. They are metabolized and eliminated according to Michaelis-Menten principle. The order could be mixed but may change to zero or first order, depending on drug concentration, frequency and route of drug administration.

CONCLUSION

Hence, pharmacokinetic-pharmacodynamic translation can be optimally achieved by incorporating, newly modified Michaelis-Menten equations into pharmacokinetic formulas for clinical efficacy and safety of the enzyme inducing and inhibiting therapeutic agents used in laboratory and clinical settings.

A novel assay based on pre-equilibrium titration curves for the determination of enzyme inhibitor binding kinetics

Selection of pharmacological agents based on potency measurements performed at equilibrium fail to incorporate the kinetic aspects of the drug–target interaction. Here we describe a method for screening or characterization of enzyme inhibitors that allows the concomitant determination of the equilibrium inhibition constant in unison with rates of complex formation and dissociation. The assay is distinct from conventional enzymatic assays and is based on the analysis of inhibition curves recorded prior to full equilibration of the system. The methodology is illustrated using bicyclic peptide inhibitors of the serine protease plasma kallikrein.

Unraveling binding mechanism and kinetics of macrocyclic Gαq protein inhibitors

G proteins represent intracellular switches that transduce signals relayed from G protein-coupled receptors. The structurally related macrocyclic depsipeptides FR900359 (FR) and YM-254890 (YM) are potent, selective inhibitors of the Gα_q protein family. We recently discovered that radiolabeled FR and YM display strongly divergent residence times, which translates into significantly longer antiasthmatic effects of FR. The present study is aimed at investigating the molecular basis for this observed disparity. Based on docking studies, we mutated amino acid residues of the Gα_q protein predicted to interact with FR or YM, and recombinantly expressed the mutated Gα_q proteins in cells in which the native Gα_q proteins had been knocked out by CRISPR-Cas9. Both radioligands showed similar association kinetics, and their binding followed a conformational selection mechanism, which was rationalized by molecular dynamics simulation studies. Several mutations of amino acid residues near the putative binding site of the "lipophilic anchors" of FR, especially those predicted to interact with the isopropyl group present in FR but not in YM, led to dramatically accelerated dissociation kinetics. Our data indicate that the long residence time of FR depends on lipophilic interactions within its binding site. The observed structure-kinetic relationships point to a complex binding mechanism of FR, which likely involves snap-lock- or dowel-like conformational changes of either ligand or protein, or both. These experimental data will be useful for the design of compounds with a desired residence time, a parameter that has now been recognized to be of utmost importance in drug development.

Impact of Target Turnover on the Translation of Drug-Target Residence Time to Time-Dependent Antibacterial Activity

The translation of time-dependent drug-target occupancy to extended pharmacological activity at low drug concentration depends on factors such as target vulnerability and the rate of target turnover. Previously, we demonstrated that the postantibiotic effect (PAE) caused by inhibitors of bacterial drug targets could be used to assess target vulnerability, and that high levels of target vulnerability coupled with relatively low rates of target resynthesis resulted in a strong correlation between drug-target residence time and the PAE following compound washout. Although the residence time of inhibitors on UDP-3-O-acyl-N-acetylglucosamine deacetylase (LpxC) in Pseudomonas aeruginosa (paLpxC) results in significant PAE, inhibitors of the equivalent enzyme in Escherichia coli (ecLpxC) do not cause a PAE. Hyperactivity of the fatty acid biosynthesis enzyme FabZ or the inclusion of sub-MIC levels of azithromycin lead to the observation of a PAE for three inhibitors of ecLpxC. FabZ hyperactivity has been shown to stabilize ecLpxC, and using mass spectrometry, we demonstrate that the appearance of a PAE can be directly linked to a 3-fold increase in the stability of ecLpxC. These studies substantiate the importance of target turnover in time-dependent drug activity.

TPX-0131, a Potent CNS-penetrant, Next-generation Inhibitor of Wild-type ALK and ALK-resistant Mutations

Since 2011, with the approval of crizotinib and subsequent approval of four additional targeted therapies, anaplastic lymphoma kinase (ALK) inhibitors have become important treatments for a subset of patients with lung cancer. Each generation of ALK inhibitor showed improvements in terms of central nervous system (CNS) penetration and potency against wild-type (WT) ALK, yet a key continued limitation is their susceptibility to resistance from ALK active-site mutations. The solvent front mutation (G1202R) and gatekeeper mutation (L1196M) are major resistance mechanisms to the first two generations of inhibitors while patients treated with the third-generation ALK inhibitor lorlatinib often experience progressive disease with multiple mutations on the same allele (mutations in cis, compound mutations). TPX-0131 is a compact macrocyclic molecule designed to fit within the ATP-binding boundary to inhibit ALK fusion proteins. In cellular assays, TPX-0131 was more potent than all five approved ALK inhibitors against WT ALK and many types of ALK resistance mutations, e.g., G1202R, L1196M, and compound mutations. In biochemical assays, TPX-0131 potently inhibited (IC50 <10 nmol/L) WT ALK and 26 ALK mutants (single and compound mutations). TPX-0131, but not lorlatinib, caused complete tumor regression in ALK (G1202R) and ALK compound mutation-dependent xenograft models. Following repeat oral administration of TPX-0131 to rats, brain levels of TPX-0131 were approximately 66% of those observed in plasma. Taken together, preclinical studies show that TPX-0131 is a CNS-penetrant, next-generation ALK inhibitor that has potency against WT ALK and a spectrum of acquired resistance mutations, especially the G1202R solvent front mutation and compound mutations, for which there are currently no effective therapies.

Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

Antibacterial agents target the products of essential genes but rarely achieve complete target inhibition. Thus, the all-or-none definition of essentiality afforded by traditional genetic approaches fails to discern the most attractive bacterial targets: those whose incomplete inhibition results in major fitness costs. In contrast, gene "vulnerability" is a continuous, quantifiable trait that relates the magnitude of gene inhibition to the effect on bacterial fitness. We developed a CRISPR interference-based functional genomics method to systematically titrate gene expression in Mycobacterium tuberculosis (Mtb) and monitor fitness outcomes. We identified highly vulnerable genes in various processes, including novel targets unexplored for drug discovery. Equally important, we identified invulnerable essential genes, potentially explaining failed drug discovery efforts. Comparison of vulnerability between the reference and a hypervirulent Mtb isolate revealed incomplete conservation of vulnerability and that differential vulnerability can predict differential antibacterial susceptibility. Our results quantitatively redefine essential bacterial processes and identify high-value targets for drug development.

Chemical tools for epichaperome-mediated interactome dysfunctions of the central nervous system

Diseases are a manifestation of how thousands of proteins interact. In several diseases, such as cancer and Alzheimer’s disease, proteome-wide disturbances in protein-protein interactions are caused by alterations to chaperome scaffolds termed epichaperomes. Epichaperome-directed chemical probes may be useful for detecting and reversing defective chaperomes. Here we provide structural, biochemical, and functional insights into the discovery of epichaperome probes, with a focus on their use in central nervous system diseases. We demonstrate on-target activity and kinetic selectivity of a radiolabeled epichaperome probe in both cells and mice, together with a proof-of-principle in human patients in an exploratory single group assignment diagnostic study (ClinicalTrials.gov Identifier: NCT03371420). The clinical study is designed to determine the pharmacokinetic parameters and the incidence of adverse events in patients receiving a single microdose of the radiolabeled probe administered by intravenous injection. In sum, we introduce a discovery platform for brain-directed chemical probes that specifically modulate epichaperomes and provide proof-of-principle applications in their use in the detection, quantification, and modulation of the target in complex biological systems.

Here, the authors show structural, biochemical, and functional insights into the discovery of epichaperome‐ directed chemical probes for use in central nervous system diseases. Probes emerging from this work have translated to human clinical studies in Alzheimer’s disease and cancer.

Learnings from past failures: Future routes of antimicrobial drug discovery

Despite the unprecedented unmet need to discover new antibiotics, only a few molecules have been registered for clinical use. This shortage is primarily based on the scientific failure in the postgenomic era of drug discovery. It appears counterintuitive that knowledge of the bacterial genome was followed by the failure to produce new antibiotics using the paradigm of target-driven drug discovery. Here, I discuss the causes of the failures and also describe how small biotech is mitigating these risks and moving forward using new strategies to identify new antibiotics.

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.

Chance Favors the Perplexed Mind: The Critical Role of Mechanistic Biochemistry in Drug Discovery

Scientific discoveries often start with an observation that does not quite make sense, within the framework of a well-established hypothesis. It is when researchers delve deeply to understand such perplexing data that established hypotheses are modified or replaced, and new and expanded knowledge of the system can be gained. This is often the case in the field of drug discovery. In this Perspective, case studies demonstrate how an understanding of perplexing data can lead to novel discoveries regarding the biological function of drug targets, or the mechanisms of compound-target interactions, that can ultimately result in new drugs entering the clinic. These case studies reinforce two interdependent themes: (1) that understanding the pathophysiological context in which drug targets function and the mechanistic details of drug-target interactions are critical to efficient and effective drug discovery and (2) that investing time and energy into following up on perplexing data can lead to novel discoveries that can drive the development of new and improved medicines.

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.