Pharmacokinetics and the drug–target residence time concept

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.

Nanoemulsions of betulinic acid stabilized with modified phosphatidylcholine increase the stability of the nanosystems and the drug's bioavailability

Betulinic acid (BA) is a natural compound with significant potential for treating various diseases, including cancer and AIDS, and possesses additional anti-inflammatory and antibacterial properties. However, its clinical application is limited because of its low solubility in water, which impairs its distribution within the body. To overcome this challenge, nanoemulsions have been developed to improve the bioavailability of such poorly soluble drugs. This study investigated modified phosphatidylcholine (PC), where some fatty acids were replaced with conjugated linoleic acid (CLA) to stabilize BA nanoemulsions. The modified PC was used to prepare nanoemulsions with droplet sizes of up to 45 nanometers. These nanoemulsions maintained stability for 60 days at room temperature (25°C±2°C) and under refrigeration (5°C±1°C), with no signs of instability. Nanoemulsions stabilized with CLA-modified PC achieved a higher drug encapsulation rate (93.5±4.3 %) than those using natural PC (82.8±4.2 %). In an in vivo model, both nanoemulsion formulations significantly increased BA absorption, with CLA-modified PC enhancing absorption by 21.3±1.3 times and natural PC by 20±2.3 times compared to the free drug. This suggests that nanoemulsions with modified PC could improve the stability and efficacy of BA in clinical applications.

Maximizing the Accuracy of Equilibrium Dissociation Constants for Affinity Complexes: From Theory to Practical Recommendations

The equilibrium dissociation constant (Kd) is a major characteristic of affinity complexes and one of the most frequently determined physicochemical parameters. Despite its significance, the values of Kd obtained for the same complex under similar conditions often exhibit considerable discrepancies and sometimes vary by orders of magnitude. These inconsistencies highlight the susceptibility of Kd determination to large systematic errors, even when random errors are small. It is imperative to both minimize and quantitatively assess the systematic errors inherent in Kd determination. Traditionally, Kd values are determined through nonlinear regression of binding isotherms. This analysis utilizes three variables: concentrations of two reactants and a fraction R of unbound limiting reactant. The systematic errors in Kd arise directly from systematic errors in these variables. Therefore, to maximize the accuracy of Kd, this study thoroughly analyzes the sources of systematic errors within the three variables, including (i) non-additive signals to calculate R, (ii) mis-calibrated experimental instruments, (iii) inaccurate calibration parameters, (iv) insufficient incubation time, (v) unsaturated binding isotherm, (vi) impurities in the reactants, and (vii) solute adsorption onto surfaces. Through this analysis, we illustrate how each source contributes to inaccuracies in the determination of Kd and propose strategies to minimize these contributions. Additionally, we introduce a method for quantitatively assessing the confidence intervals of systematic errors in concentrations, a crucial step toward quantitatively evaluating the accuracy of Kd. While presenting original findings, this paper also reiterates the fundamentals of Kd determination, hence guiding researchers across all proficiency levels. By shedding light on the sources of systematic errors and offering strategies for their mitigation, our work will help researchers enhance the accuracy of Kd determination, thereby making binding studies more reliable and the conclusions drawn from such studies more robust.

When to consider intra-target microdosing: physiologically based pharmacokinetic modeling approach to quantitatively identify key factors for observing target engagement

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM.

Pseudoirreversible inhibition elicits persistent efficacy of a sphingosine 1-phosphate receptor 1 antagonist

Sphingosine 1-phosphate receptor 1 (S1PR1), a G protein-coupled receptor, is required for lymphocyte trafficking, and is a promising therapeutic target in inflammatory diseases. Here, we synthesize a competitive S1PR1 antagonist, KSI-6666, that effectively suppresses pathogenic inflammation. Metadynamics simulations suggest that the interaction of KSI-6666 with a methionine residue Met124 in the ligand-binding pocket of S1PR1 may inhibit the dissociation of KSI-6666 from S1PR1. Consistently, in vitro functional and mutational analyses reveal that KSI-6666 causes pseudoirreversible inhibition of S1PR1, dependent on the Met124 of the protein and substituents on the distal benzene ring of KSI-6666. Moreover, in vivo study suggests that this pseudoirreversible inhibition is responsible for the persistent activity of KSI-6666.

Novel 6-alkyl-bridged 4-arylalkylpiperazin-1-yl derivatives of azepino[4,3-b]indol-1(2H)-one as potent BChE-selective inhibitors showing protective effects against neurodegenerative insults

Due to the putative role of butyrylcholinesterase (BChE) in regulation of acetylcholine levels and functions in the late stages of the Alzheimer's disease (AD), the potential of selective inhibitors (BChEIs) has been envisaged as an alternative to administration of acetylcholinesterase inhibitors (AChEIs). Starting from our recent findings, herein the synthesis and in vitro evaluation of cholinesterase (ChE) inhibition of a novel series of some twenty 3,4,5,6-tetrahydroazepino[4,3-b]indol-1(2H)-one derivatives, bearing at the indole nitrogen diverse alkyl-bridged 4-arylalkylpiperazin-1-yl chains, are reported. The length of the spacers, as well as the type of arylalkyl group affected the enzyme inhibition potency and BChE/AChE selectivity. Two compounds, namely 14c (IC50 = 163 nM) and 14d (IC50 = 65 nM), bearing at the nitrogen atom in position 6 a n-pentyl- or n-heptyl-bridged 4-phenethylpiperazin-1-yl chains, respectively, proved to be highly potent mixed-type inhibitors of both equine and human BChE isoforms, showing more than two order magnitude of selectivity over AChE. The study of binding kinetics through surface plasmon resonance (SPR) highlighted differences in their BChE residence times (8 and 47 s for 14c and 14d, respectively). Moreover, 14c and 14d proved to hit other mechanisms known to trigger neurodegeneration underlying AD and other CNS disorders. Unlike 14c, compound 14d proved also capable of inhibiting by more than 60% the in vitro self-induced aggregation of neurotoxic amyloid-β (Aβ) peptide at 100 μM concentration. On the other hand, 14c was slightly better than 14d in counteracting, at 1 and 10 μM concentration, glutamate excitotoxicity, due to over-excitation of NMDA receptors, and hydrogen peroxide-induced oxidative stress assessed in neuroblastoma cell line SH-SY5Y. This paper is dedicated to Prof. Marcello Ferappi, former dean of the Faculty of Pharmacy of the University of Bari, in the occasion of his 90th birthday.

Preclinical evaluation of the SARS-CoV-2 Mpro inhibitor RAY1216 shows improved pharmacokinetics compared with nirmatrelvir

Although vaccines are available for SARS-CoV-2, antiviral drugs such as nirmatrelvir are still needed, particularly for individuals in whom vaccines are less effective, such as the immunocompromised, to prevent severe COVID-19. Here we report an α-ketoamide-based peptidomimetic inhibitor of the SARS-CoV-2 main protease (Mpro), designated RAY1216. Enzyme inhibition kinetic analysis shows that RAY1216 has an inhibition constant of 8.4 nM and suggests that it dissociates about 12 times slower from Mpro compared with nirmatrelvir. The crystal structure of the SARS-CoV-2 Mpro:RAY1216 complex shows that RAY1216 covalently binds to the catalytic Cys145 through the α-ketoamide group. In vitro and using human ACE2 transgenic mouse models, RAY1216 shows antiviral activities against SARS-CoV-2 variants comparable to those of nirmatrelvir. It also shows improved pharmacokinetics in mice and rats, suggesting that RAY1216 could be used without ritonavir, which is co-administered with nirmatrelvir. RAY1216 has been approved as a single-component drug named 'leritrelvir' for COVID-19 treatment in China.

An orally bioavailable SARS-CoV-2 main protease inhibitor exhibits improved affinity and reduced sensitivity to mutations

Inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function against these mutants are thus urgently needed. We hypothesized that the covalent hepatitis C virus protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more efficiently than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to those of NTV. A crucial feature of ML2006a4 is a derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Last, ML2006a4 was found to be less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory design can preemptively address potential resistance mechanisms to expand future treatment options against coronavirus variants.

Exhaustively Exploring the Prevalent Interaction Pathways of Ligands Targeting the Ligand-Binding Pocket of Farnesoid X Receptor via Combined Enhanced Sampling

It is well-known that the potency of a drug is heavily associated with its kinetic and thermodynamic properties with the target. Nuclear receptors (NRs), as an important target family, play important roles in regulating a variety of physiological processes in vivo. However, it is hard to understand the drug-NR interaction process because of the closed structure of the ligand-binding domain (LBD) of the NR proteins, which apparently hinders the rational design of drugs with controllable kinetic properties. Therefore, understanding the underlying mechanism of the ligand-NR interaction process seems necessary to help NR drug design. However, it is usually difficult for experimental approaches to interpret the kinetic process of drug-target interactions. Therefore, in silico methods were utilized to explore the optimal binding/dissociation pathways of the NR ligands. Specifically, farnesoid X receptor (FXR) is considered here as the target system since it has been an important target for the treatment of bile acid metabolism-associated diseases, and a series of structures cocrystallized with diverse scaffold ligands were resolved. By using random acceleration molecular dynamics (RAMD) simulation and umbrella sampling (US), 5 main dissociation pathways (pathways I-V) were identified in 11 representative FXR ligands, with most of them (9/11) preferring to go through Pathway III and the remaining two favoring escaping from Pathway I and IV. Furthermore, key residues functioning in the three main dissociation pathways were revealed by the kinetic residue energy analysis (KREA) based on the US trajectories, which may serve as road-marker residues for rapid identification of the (un)binding pathways of FXR ligands. Moreover, the preferred pathways explored by RAMD simulations are in good agreement with the minimum free energy path identified by the US simulations with the Pearson R = 0.76 between the predicted binding affinity and the experimental data, suggesting that RAMD is suitable for applying in large-scale (un)binding-pathway exploration in the case of ligands with obscure binding tunnels to the target.

A novel, label-free, pre-equilibrium assay to determine the association and dissociation rate constants of therapeutic antibodies on living cells

BACKGROUND AND PURPOSE

Monoclonal antibodies (Ab) represent the fastest growing drug class. Knowledge of the biophysical parameters (kon , koff and KD ) that dictate Ab:receptor interaction is critical during the drug discovery process. However, with the increasing complexity of Ab formats and their targets, it became apparent that existing technologies present limitations and are not always suitable to determine these parameters. Therefore, novel affinity determination methods represent an unmet assay need.

EXPERIMENTAL APPROACH

We developed a pre-equilibrium kinetic exclusion assay using recent mathematical advances to determine the kon , koff and KD of monoclonal Ab:receptor interactions on living cells. The assay is amenable to all human IgG1 and rabbit Abs.

KEY RESULTS

Using our novel assay, we demonstrated for several monoclonal Ab:receptor pairs that the calculated kinetic rate constants were comparable with orthogonal methods that were lower throughput or more resource consuming. We ran simulations to predict the critical conditions to improve the performance of the assays. We further showed that this method could successfully be applied to both suspension and adherent cells. Finally, we demonstrated that kon and koff , but not KD , correlate with in vitro potency for a panel of monoclonal Abs.

CONCLUSIONS AND IMPLICATIONS

Our novel assay has the potential to systematically probe binding kinetics of monoclonal Abs to cells and can be incorporated in a screening cascade to identify new therapeutic candidates. Wide-spread adoption of pre-equilibrium assays using physiologically relevant systems will lead to a more holistic understanding of how Ab binding kinetics influence their potency.

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.

Evolution of Slow-Binding Inhibitors Targeting Histone Deacetylase Isoforms

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

Degradation of MK2 with natural compound andrographolide: A new modality for anti-inflammatory therapy

The p38MAPK-MK2 signaling axis functions as an initiator of inflammation. Targeting the p38MAPK-MK2 signaling axis represents a direct therapeutic intervention of inflammatory diseases. We described here a novel role of andrographolide (AG), a small-molecule ent-labdane natural compound, as an inhibitor of p38MAPK-MK2 axis via MK2 degradation. AG was found to bind to the activation loop of MK2, located at the interface of the p38MAPK-MK2 biomolecular complex. This interaction disrupted the complex formation and predisposed MK2 to proteasome-mediated degradation. We showed that AG induced MK2 degradation in a concentration- and time-dependent manner and exerted its anti-inflammatory effects by enhancing the mRNA-destabilizing activity of tristetraprolin, thereby inhibiting pro-inflammatory mediator production (e.g., TNF-α, MCP-1). Administration of AG via intratracheal (i.t.) route to mice induced MK2 downregulation in lung alveolar macrophages, but not lung tissues, and prevented macrophage activation. Our study also demonstrated that the anti-inflammatory effects achieved by AG via MK2 degradation were more durable and sustained than that achieved by the conventional MK2 kinase inhibitors (e.g., PF-3644022). Taken together, our findings illustrated a novel mode of action of AG by modulating the p38MAPK-MK2 signaling axis and would pave the way for the development of a novel class of anti-inflammatory agents targeting MK2 for degradation by harnessing the privileged scaffold of AG.

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses

Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (r_p > 0.9) for MZ1 in different Brd^BD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the Brd^BD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

Inhibition of 3-Hydroxykynurenine Transaminase from Aedes aegypti and Anopheles gambiae: A Mosquito-Specific Target to Combat the Transmission of Arboviruses

Arboviral infections such as Zika, chikungunya, dengue, and yellow fever pose significant health problems globally. The population at risk is expanding with the geographical distribution of the main transmission vector of these viruses, the Aedes aegypti mosquito. The global spreading of this mosquito is driven by human migration, urbanization, climate change, and the ecological plasticity of the species. Currently, there are no specific treatments for Aedes-borne infections. One strategy to combat different mosquito-borne arboviruses is to design molecules that can specifically inhibit a critical host protein. We obtained the crystal structure of 3-hydroxykynurenine transaminase (AeHKT) from A. aegypti, an essential detoxification enzyme of the tryptophan metabolism pathway. Since AeHKT is found exclusively in mosquitoes, it provides the ideal molecular target for the development of inhibitors. Therefore, we determined and compared the free binding energy of the inhibitors 4-(2-aminophenyl)-4-oxobutyric acid (4OB) and sodium 4-(3-phenyl-1,2,4-oxadiazol-5-yl)butanoate (OXA) to AeHKT and AgHKT from Anopheles gambiae, the only crystal structure of this enzyme previously known. The cocrystallized inhibitor 4OB binds to AgHKT with K _i of 300 μM. We showed that OXA binds to both AeHKT and AgHKT enzymes with binding energies 2-fold more favorable than the crystallographic inhibitor 4OB and displayed a 2-fold greater residence time τ upon binding to AeHKT than 4OB. These findings indicate that the 1,2,4-oxadiazole derivatives are inhibitors of the HKT enzyme not only from A. aegypti but also from A. gambiae.

Folic acid-decorated alginate nanoparticles loaded hydrogel for the oral delivery of diferourylmethane in colorectal cancer

The disease-related suffering in colorectal cancer remains prevalent despite advancements in the field of drug delivery. Chemotherapy-related side effects and non-specificity remain a challenge in drug delivery. The great majority of hydrophobic drugs cannot be successfully delivered to the colon orally mainly due to poor solubility, low bioavailability, pH differences, and food interactions. Polymeric nanoparticles are potential drug delivery candidates but there are numerous limitations to their usefulness in colon cancer. The nanoparticles are removed from the body rapidly by p-glycoprotein efflux, inactivation, or breakdown by enzymes limiting their efficiency. Furthermore, there is a lack of selectivity in targeting cancer cells; nanoparticles may also target healthy cells, resulting in toxicity and adverse effects. The study aimed to use nanoparticles for specific targeting of the colorectal tumor cells via the oral route of administration without adverse effects. Folic acid (FA), a cancer-targeting ligand possessing a high affinity for folate receptors overexpressed in colorectal cancers was conjugated to sodium alginate- nanoparticles by NH₂-linkage. The folic-acid conjugated nanoparticles (FNPs) were delivered to the colon by a pH-sensitive hydrogel synthesized by the free radical polymerization method to provide sustained drug release. The developed system referred to as the "Hydrogel-Nano (HN) drug delivery system," was specifically capable of delivering diferourylmethane to the colon. The HN system was characterized by DLS, FTIR, XRD, TGA, DSC, and SEM. The FNPs size, polydispersity index, and zeta potential were measured. The folic acid-conjugation to nanoparticles' surface was studied by UV-visible spectroscopy using Beer-Lambert's law. In-vitro studies, including sol-gel, porosity, drug loading, entrapment efficiency, etc., revealed promising results. The swelling and release studies showed pH-dependent release of the drug in colonic pH 7.4. Cellular uptake and cytotoxicity studies performed on FR-overexpressed Hela cell lines and FR-negative A-549 cell lines showed facilitated uptake of nanoparticles by folate receptors. A threefold increase in C_max and prolongation of the mean residence time (MRT) to 14.52 +/- 0.217 h indicated sustained drug release by the HN system. The findings of the study can provide a sufficient ground that the synergistic approach of the HN system can deliver hydrophobic drugs to colorectal cancer cells via the oral route, but further in-vivo animal cancer model studies are required.

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.

Allosteric binding cooperativity in a kinetic context

Allosteric modulators are of prime interest in drug discovery. These drugs regulate the binding and function of endogenous ligands, with some advantages over orthosteric ligands. A typical pharmacological parameter in allosteric modulation is binding cooperativity. This property can yield unexpected but illuminating results when decomposed into its kinetic parameters. Using two reference models (the allosteric ternary complex receptor model and a heterodimer receptor model), a relationship has been derived for the cooperativity rate constant parameters. This relationship allows many combinations of the cooperativity kinetic parameters for a single binding cooperativity value obtained under equilibrium conditions. This assessment may help understand striking experimental results involving allosteric modulation and suggest further investigations in the field.

High-throughput mechanistic screening of non-equilibrium inhibitors by a fully automated data analysis pipeline in early drug-discovery

Recent efforts for increasing the success in drug discovery focus on an early, massive, and routine mechanistic and/or kinetic characterization of drug-target engagement as part of a design-make-test-analyze strategy. From an experimental perspective, many mechanistic assays can be translated into a scalable format on automation platforms and thereby enable routine characterization of hundreds or thousands of compounds. However, now the limiting factor to achieve such in-depth characterization at high-throughput becomes the quality-driven data analysis, the sheer scale of which outweighs the time available to the scientific staff of most labs. Therefore, automated analytical workflows are needed to enable such experimental scale-up. We have implemented such a fully automated workflow in Genedata Screener for time-dependent ligand-target binding analysis to characterize non-equilibrium inhibitors. The workflow automates Quality Control (QC) / data modelling and decision-making process in a staged analysis: (1) quality control of raw input data-fluorescence signal-based progress curves - featuring automated rejection of unsuitable measurements; (2) automated model selection - one-step versus two-step binding model - using statistical methods and biological validity rules; (3) result visualization in specific plots and annotated result tables, enabling the scientist to review large result sets efficiently and, at the same time, to rapidly identify and focus on interesting or unusual results; (4) an interactive user interface for immediate adjustment of automated decisions, where necessary. Applying this workflow to first-pass, high-throughput kinetic studies on kinase projects has allowed us to surmount previously rate-limiting manual analysis steps and boost productivity; and is now routinely embedded in a biopharma discovery research process.

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.

Insight into Thermodynamic and Kinetic Profiles in Small-Molecule Optimization

Structure-activity relationships (SARs) and structure-property relationships (SPRs) have been considered the most important factors during the drug optimization process. For medicinal chemists, improvements in the potencies and druglike properties of small molecules are regarded as their major goals. Among them, the binding affinity and selectivity of small molecules on their targets are the most important indicators. In recent years, there has been growing interest in using thermodynamic and kinetic profiles to analyze ligand-receptor interactions, which could provide not only binding affinities but also detailed binding parameters for small-molecule optimization. In this perspective, we are trying to provide an insight into thermodynamic and kinetic profiles in small-molecule optimization. Through a highlight of strategies on the small-molecule optimization with specific cases, we aim to put forward the importance of structure-thermodynamic relationships (STRs) and structure-kinetic relationships (SKRs), which could provide more guidance to find safe and effective small-molecule drugs.

Chitosan/guar gum-based thermoreversible hydrogels loaded with pullulan nanoparticles for enhanced nose-to-brain drug delivery

The biopolymers-based two-fold system could provide a sustained release platform for drug delivery to the brain resisting the mucociliary clearance, enzymatic degradation, bypassing the first-pass hepatic metabolism, and BBB thus providing superior bioavailability through intranasal administration. In this study, poloxamers PF-127/PF-68 grafted chitosan HCl-co-guar gum-based thermoresponsive hydrogel loaded with eletriptan hydrobromide laden pullulan nanoparticles was synthesized and subjected to dynamic light scattering, Fourier transform infrared spectroscopy, thermal analysis, x-ray diffraction, scanning electron microscopy, stability studies, mucoadhesive strength and time, gel strength, cloud point assessment, rheological assessment, ex-vivo permeation, cell viability assay, histology studies, and in-vivo Pharmacokinetics studies, etc. It is quite evident that CSG-EH-NPs T-Hgel has an enhanced sustained release drug profile where approximately 86 % and 84 % of drug released in phosphate buffer saline and simulated nasal fluid respectively throughout 48 h compared to EH-NPs where 99.44 % and 97.53 % of the drug was released in PBS and SNF for 8 h. In-vivo PKa parameters i.e., mean residence time (MRT) of 11.9 ± 0.83 compared to EH-NPs MRT of 10.2 ± 0.92 and area under the curve (AUC_tot) of 42,540.5 ± 5314.14 comparing to AUC_tot of EH-NPs 38,026 ± 6343.1 also establish the superiority of CSG-EH-NPs T-Hgel.

Binding of the B-Raf Inhibitors Dabrafenib and Vemurafenib to Human Serum Albumin: A Biophysical and Molecular Simulation Study

Drug binding to human serum albumin (HSA) significantly affects in vivo drug transport and biological activity. To gain insight into the binding mechanism of the two B-Raf tyrosine kinase inhibitors dabrafenib and vemurafenib to HSA, in this work, we adopted a combined strategy based on fluorescence spectroscopy, isothermal titration calorimetry (ITC), circular dichroism (CD), and molecular simulations. Both anticancer drugs are found to bind spontaneously and with a 1:1 stoichiometry within the same binding pocket, located in Sudlow's site II (subdomain IIIA) of the protein with comparable affinity and without substantially perturbing the protein secondary structure. However, the nature of each drug-protein interactions is distinct: whereas the formation of the dabrafenib/HSA complex is more entropically driven, the formation of the alternative vemurafenib/HSA assembly is prevalently enthalpic in nature. Kinetic analysis also indicates that the association rate is similar for the two drugs, whereas the residence time of vemurafenib within the HSA binding pocket is somewhat higher than that determined for the alternative B-Raf inhibitor.

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.

Resolving the deceptive isoform and complex selectivity of HDAC1/2 inhibitors

The histone deacetylase paralogs HDAC1/2/3 and their corepressor complexes serve as epigenetic master regulators of chromatin function. Over the past decades, HDACs have been widely pursued as pharmacological targets, and considerable efforts have been invested in the development of small molecule drugs. Specifically, ortho-aminoanilide-derived inhibitors, including CI-994 and Cpd-60, stand out with their attractive selectivity profiles and have been used extensively as tools to delineate the biological roles of specific HDAC isoforms and complexes. Here, we apply a suite of activity-independent strategies to investigate how dynamic processes that regulate HDAC complexes govern the isoform and complex selectivity of HDAC inhibitors. Importantly, we find that overreliance on static and simplified biochemical activity assays has confounded the determination of the biological selectivity of these ligands. Our data urge a comprehensive reinterpretation of numerous studies utilizing these tool compounds for the interrogation of epigenetic and other cellular processes.

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.

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand–protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study

During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT₇ receptor (5-HT₇R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT₇R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT₇R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT₇R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT₇R ligands that can be helpful to design new 5-HT₇R ligands with fine-tuning of the kinetic profile.

Development of the First Covalent Monopolar Spindle Kinase 1 (MPS1/TTK) Inhibitor

Monopolar spindle kinase 1 (MPS1/TTK) is a key element of the mitotic checkpoint and clinically evaluated as a target in the treatment of aggressive tumors such as triple-negative breast cancer. While long drug-target residence times have been suggested to be beneficial in the context of therapeutic MPS1 inhibition, no irreversible inhibitors have been reported. Here we present the design and characterization of the first irreversible covalent MPS1 inhibitor, RMS-07, targeting a poorly conserved cysteine in the kinase's hinge region. RMS-07 shows potent MPS1 inhibitory activity and selectivity against all protein kinases with an equivalent cysteine but also in a broader kinase panel. We demonstrate potent cellular target engagement and pronounced activity against various cancer cell lines. The covalent binding mode was validated by mass spectrometry and an X-ray crystal structure. This proof of MPS1 covalent ligandability may open new avenues for the design of MPS1-specific chemical probes or drugs.

Predicting Residence Time of GPCR Ligands with Machine Learning

Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug discovery, machine learning models that can predict that value need to be developed. One of the main challenges with predicting residence time is the paucity of data. This chapter outlines all of the currently available ligand kinetic data, providing a repository that contains the largest publicly available source of GPCR-ligand kinetic data to date. To help decipher the features of kinetic data that might be beneficial to include in computational models for the prediction of residence time, the experimental evidence for properties that influence residence time are summarized. Finally, two different workflows for predicting residence time with machine learning are outlined. The first is a single-target model trained on ligand features; the second is a multi-target model trained on features generated from molecular dynamics simulations.

Assessment of the Kochak-Benet Equation for Hepatic Clearance for the Parallel-Tube Model: Relevance of Classic Clearance Concepts in PK and PBPK

This report reviews concepts related to operation of the classic parallel-tube model (PTM) for hepatic disposition and examines two recent proposals of a newly derived equation to describe hepatic clearance (CL_H). It is demonstrated that the proposed equation is identical to a re-arrangement of an earlier relationship from Pang and Rowland and provides a means of calculation of intrinsic clearance (CL_int,PTM) rather than CL_H as posed. We further demonstrate how classic hepatic clearance models with an assumed CL_int, while subject to numerous limitations, remain highly useful and necessary in both traditional pharmacokinetics (PK) and physiologically based pharmacokinetic (PBPK) modeling.

Mutation in Abl kinase with altered drug-binding kinetics indicates a novel mechanism of imatinib resistance

Protein kinase inhibitors are potent anticancer therapeutics. For example, the Bcr-Abl kinase inhibitor imatinib decreases mortality for chronic myeloid leukemia by 80%, but 22 to 41% of patients acquire resistance to imatinib. About 70% of relapsed patients harbor mutations in the Bcr-Abl kinase domain, where more than a hundred different mutations have been identified. Some mutations are located near the imatinib-binding site and cause resistance through altered interactions with the drug. However, many resistance mutations are located far from the drug-binding site, and it remains unclear how these mutations confer resistance. Additionally, earlier studies on small sets of patient-derived imatinib resistance mutations indicated that some of these mutant proteins were in fact sensitive to imatinib in cellular and biochemical studies. Here, we surveyed the resistance of 94 patient-derived Abl kinase domain mutations annotated as disease relevant or resistance causing using an engagement assay in live cells. We found that only two-thirds of mutations weaken imatinib affinity by more than twofold compared to Abl wild type. Surprisingly, one-third of mutations in the Abl kinase domain still remain sensitive to imatinib and bind with similar or higher affinity than wild type. Intriguingly, we identified three clinical Abl mutations that bind imatinib with wild type-like affinity but dissociate from imatinib considerably faster. Given the relevance of residence time for drug efficacy, mutations that alter binding kinetics could cause resistance in the nonequilibrium environment of the body where drug export and clearance play critical roles.

KD determination from time-resolved experiments on live cells with LigandTracer and reconciliation with end-point flow cytometry measurements

Design of next-generation therapeutics comes with new challenges and emulates technology and methods to meet them. Characterizing the binding of either natural ligands or therapeutic proteins to cell-surface receptors, for which relevant recombinant versions may not exist, represents one of these challenges. Here we report the characterization of the interaction of five different antibody therapeutics (Trastuzumab, Rituximab, Panitumumab, Pertuzumab, and Cetuximab) with their cognate target receptors using LigandTracer. The method offers the advantage of being performed on live cells, alleviating the need for a recombinant source of the receptor. Furthermore, time-resolved measurements, in addition to allowing the determination of the affinity of the studied drug to its target, give access to the binding kinetics thereby providing a full characterization of the system. In this study, we also compared time-resolved LigandTracer data with end-point K_D determination from flow cytometry experiments and hypothesize that discrepancies between these two approaches, when they exist, generally come from flow cytometry titration curves being acquired prior to full equilibration of the system. Our data, however, show that knowledge of the kinetics of the interaction allows to reconcile the data obtained by flow cytometry and LigandTracer and demonstrate the complementarity of these two methods.

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.

Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning

In the field of drug-target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task. These methods used binary drug-target interaction datasets to train their models. The prediction of drug-target interactions is inherently a regression problem and these interactions would be identified according to the binding affinity between drugs and targets. This paper deals the binary drug-target interactions and tries to identify the binary interactions based on the binding strength of a drug and its target. To this end, we propose a semi-supervised transfer learning approach to predict the binding affinity in a continuous spectrum for binary interactions. Due to the lack of training data with continuous binding affinity in the target domain, the proposed method makes use of the information available in other domains (i.e. source domain), via the transfer learning approach. The general framework of our algorithm is based on an objective function, which considers the performance in both source and target domains as well as the unlabeled data in the target domain via a regularization term. To optimize this objective function, we make use of a gradient boosting machine which constructs the final model. To assess the performance of the proposed method, we have used some benchmark datasets with binary interactions for four classes of human proteins. Our algorithm identifies interactions in a more realistic situation. According to the experimental results, our regression model performs better than the state-of-the-art methods in some procedures.

Curcumin-laden hyaluronic acid-co-Pullulan-based biomaterials as a potential platform to synergistically enhance the diabetic wound repair

Injectable hydrogel with multifunctional tunable properties comprising biocompatibility, anti-oxidative, anti-bacterial, and/or anti-infection are highly preferred to efficiently promote diabetic wound repair and its development remains a challenge. In this study, we report hyaluronic acid and Pullulan-based injectable hydrogel loaded with curcumin that could potentiate reepithelization, increase angiogenesis, and collagen deposition at wound microenvironment to endorse healing cascade compared to other treatment groups. The physical interaction and self-assembly of hyaluronic acid-Pullulan-grafted-pluronic F127 injectable hydrogel were confirmed using nuclear magnetic resonance (¹H NMR) and Fourier transformed infrared spectroscopy (FT-IR), and cytocompatibility was confirmed by fibroblast viability assay. The CUR-laden hyaluronic acid-Pullulan-g-F127 injectable hydrogel promptly undergoes a sol-gel transition and has proved to potentiate wound healing in a streptozotocin-induced diabetic rat model by promoting 93% of wound closure compared to other groups having 35%, 38%, and 62%. The comparative in vivo study and histological examination was conducted which demonstrated an expeditious recovery rate by significantly reducing the wound healing days i.e. 35 days in a control group, 33 days in the CUR suspension group, 21 days in unloaded injectable, and 13 days was observed in CUR loaded hydrogel group. Furthermore, we suggest that the injectable hydrogel laden with CUR showed a prompt wound healing potential by increasing the cell proliferation and serves as a drug delivery platform for sustained and targeted delivery of hydrophobic moieties.

The Problems of Applying Classical Pharmacology Analysis to Modern In Vitro Drug Discovery Assays: Slow Binding Kinetics and High Target Concentration

The analysis framework used to quantify drug potency in vitro (e.g., K_d or K_i) was initially developed for classical pharmacology bioassays, for example, organ bath experiments testing moderate-affinity natural products. Modern drug discovery can infringe the assumptions of the classical pharmacology analysis equations, owing to the reduction of assay volume in miniaturization, target overexpression, and the increase of compound-target affinity in medicinal chemistry. These assumptions are that (1) the compound concentration greatly exceeds the target concentration (i.e., minimal ligand depletion), and (2) the compound is at equilibrium with the receptor (i.e., rapid ligand binding kinetics). Unappreciated infringement of these assumptions can lead to substantial underestimation of compound affinity, which negatively impacts the drug discovery process, from early-stage lead optimization to prediction of human dosing. This study evaluates the real-world impact of these factors on the target interaction assays used in drug discovery using literature examples, database searches, and simulations. The ranges of compound affinity and the assay types that are prone to depletion and equilibration artifacts are identified. Importantly, the highest-affinity compounds, usually the highest value chemical matter in drug discovery, are the most affected. Methods and simulation tools are provided to enable investigators to evaluate, manage, and minimize depletion or equilibration artifacts. This study enables the correct application of pharmacological data analysis to accurately quantify affinity using modern drug discovery assay technology.

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

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

Improved drug delivery and accelerated diabetic wound healing by chondroitin sulfate grafted alginate-based thermoreversible hydrogels

Injectable hydrogels with multifunctional tunable properties comprising biocompatibility, anti-oxidative, anti-bacterial, and/or anti-infection are highly preferred to efficiently promote diabetic wound repair and its development remains a challenge. In this study, we report chondroitin sulphate (CS) and sodium alginate (SA)-based injectable hydrogel using solvent casting method loaded with curcumin that could potentiate reepithelization, increase angiogenesis, and collagen deposition at wound microenvironment to endorse healing cascade. The physical interaction and self-assembly of chondroitin sulfate grafted alginate (CS-Alg-g-PF127) hydrogel were confirmed using nuclear magnetic resonance (¹H NMR) and Fourier transformed infrared spectroscopy (FT-IR), and cytocompatibility was confirmed by fibroblast viability assay. The Masson's trichrome (MT) and hematoxylin and eosin (H&E) results revealed that blank chondroitin sulfate grafted alginate (CS-Alg-g-PF127) and CUR loaded CS-Alg-g-PF127 hydrogel had promising tissue regenerative ability, and showing enhanced wound healing compared to other treatment groups. The controlled release of CUR from injectable hydrogel was evaluated by drug release studies and pharmacokinetic profile (PK) using high-performance liquid chromatography (HPLC) that exhibited the mean residence time (MRT) and area under the curve (AUC) was increased up to 16.18 h and 203.64 ± 30.1 μg/mL*h, respectively. Cytotoxicity analysis of the injectable hydrogels using 3 T3-L1 fibroblasts cells and in vivo toxicity evaluated by subcutaneous injection for 24 h followed by histological examination, confirmed good biocompatibility of CUR loaded CS-Alg-g-PF127 hydrogel. Interestingly, the results of in vivo wound healing by injectable hydrogel showed the upregulation of fibroblasts-like cells, collagen deposition, and differentiated keratinocytes stimulating dermo-epidermal junction, which might endorse that they are potential candidates for excisional wound healing models.

Accurate quantitative determination of affinity and binding kinetics for tight binding inhibition of xanthine oxidase

The accurate quantitative determination of affinity and binding kinetics (BK) for tight binding inhibition is extraordinary important from both the continuous optimization of compounds, particularly in developing structure-activity relationships (SAR), and the prediction of in vivo target occupancy (TO). Due to the unique properties for tight binding inhibition that the inhibitors are characterized by the ultrahigh-affinity, relatively fast association to the target enzyme combined with extremely slow dissociation of the inhibitor-enzyme binary complex, the classical steady state equilibrium methods are no longer valid. Here, we made several recommendations of how to design the optimal experiments and apply special mathematical calculation approaches to quantitatively evaluate the accurate affinity and BK as the examples of two tight binding inhibitors against the xanthine oxidase (XO), as well as compared the differences in the results calculated from the different data analytical methods and analyzed the influence of these differences on the XO engagement in human. Analysis of the results displayed that the accurate apparent dissociation constant (K_i^*,app) was 0.2 ± 0.06 nM for topiroxotstat and was 0.45 ± 0.2 nM for febuxostat; that on-rate (k_on) was (4.3 ± 1.1) × 10⁶ M^-1s^-1 for topiroxotstat and was(133.3 ± 3.5) × 10⁶ M^-1s^-1 for febuxostat, and off-rate (k_off) was (1.0±0.2) × 10^-5 s^-1 for topiroxotstat and was ≤ 0.16 × 10^-5 s^-1for febuxostat. Moreover, there were significant differences in the K_i^*,app and k_off values estimated using the appropriate specialized methods for tight binding inhibition versus classical steady state equilibrium methods, with the substantial differences of 14-fold and 32-fold reduction for topiroxostat, respectively, and of 9.6-fold and ≥ 213-fold reduction for febuxostat, while the k_on values remain the moderate differences for the two inhibitors. The obvious greater AUC of XO engagement time courses and longer durations of above 70% engagement by the appropriate specialized methods for tight binding inhibition were observed that the results display the differences of 70.1% and 88%, respectively for topiroxostat and of 38.1% and 35.0%, respectively for febuxostat in human liver cell than by classical steady state equilibrium methods. Again, our studies provide several valuable recommendations of the optimal experiment protocols and appropriate analytical approaches for accurately quantitatively assessing the affinity and BK parameters as well as demonstrate the ability of our recommended methods to generate reliable data for tight binding inhibitors against XO.

Deciphering the Agonist Binding Mechanism to the Adenosine A1 Receptor

Despite being among the most characterized G protein-coupled receptors (GPCRs), adenosine receptors (ARs) have always been a difficult target in drug design. To date, no agonist other than the natural effector and the diagnostic regadenoson has been approved for human use. Recently, the structure of the adenosine A1 receptor (A1R) was determined in the active, Gi protein complexed state; this has important repercussions for structure-based drug design. Here, we employed supervised molecular dynamics simulations and mutagenesis experiments to extend the structural knowledge of the binding of selective agonists to A1R. Our results identify new residues involved in the association and dissociation pathway, they suggest the binding mode of N6-cyclopentyladenosine (CPA) related ligands, and they highlight the dramatic effect that chemical modifications can have on the overall binding mechanism, paving the way for the rational development of a structure-kinetics relationship of A1R agonists.

The exploration of aza-quinolines as hematopoietic prostaglandin D synthase (H-PGDS) inhibitors with low brain exposure

GlaxoSmithKline and Astex Pharmaceuticals recently disclosed the discovery of the potent H-PGDS inhibitor GSK2894631A 1a (IC₅₀ = 9.9 nM) as part of a fragment-based drug discovery collaboration with Astex Pharmaceuticals. This molecule exhibited good murine pharmacokinetics, allowing it to be utilized to explore H-PGDS pharmacology in vivo. Yet, with prolonged dosing at higher concentrations, 1a induced CNS toxicity. Looking to attenuate brain penetration in this series, aza-quinolines, were prepared with the intent of increasing polar surface area. Nitrogen substitutions at the 6- and 8-positions of the quinoline were discovered to be tolerated by the enzyme. Subsequent structure activity studies in these aza-quinoline scaffolds led to the identification of 1,8-naphthyridine 1y (IC₅₀ = 9.4 nM) as a potent peripherally restricted H-PGDS inhibitor. Compound 1y is efficacious in four in vivo inflammatory models and exhibits no CNS toxicity.

Toward in vivo relevant drug design

Current early and preclinical drug discovery are rooted in decades-old empirical principles describing structure-free energy and structure-function relationships under equilibrium conditions that frequently break down under in vivo conditions. Improved prediction of efficacy and toxicity depends on a paradigm shift to in vivo-relevant principles describing the true nonequilibrium/nonlinear dynamic (NLD) nature of cellular systems. Here, we outline a holistic, in vivo-relevant first principles theory ('Biodynamics'), in which cellular function/dysfunction, and pharmaco-/toxicodynamic effects are considered as emergent behaviors of multimolecular systems powered by covalent and noncovalent free energy sources. The reduction to practice of Biodynamics theory consists of in silico simulations performed at the atomistic and molecular systems levels, versus empirical models fit to in vitro data under the classical paradigm.