Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients

Physiologically Based Pharmacokinetic Model of Plasma and Intracranial Pharmacokinetics and CDK4/6 Occupancy of Abemaciclib to Optimizing Dosing Regimen for Brain Metastatic Patients

Objective: The study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict steady state trough concentrations (C min) and CDK4/6 occupancy in plasma and cerebrospinal fluid (CSF) for abemaciclib (ABE) and its three active metabolites. Additionally, a biomarker model was constructed to simulate changes in pRB and TOPO-IIα expression. Methods: The population PBPK and biomarker models of ABE were developed using physicochemical, pharmacokinetics (PK), CDK4/6 occupancy, biomarker, and physiological properties. These models were then validated using four clinical plasma PK studies, two CSF PK studies, and one clinically observed biomarker expression change in patients. Results: The PBPK model showed good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.5 to 2.0 for AUC, C max, C min in plasma and CSF. Key factors affecting C min and CDK4/6 occupancy for total analytes (sum of ABE and metabolites) were identified as CYP3A4, ABCB1, ABCG2 expression, and plasma albumin levels. PBPK simulations suggested that the optimal dosing regimen for ABE in brain metastatic breast cancer (MBC) is either 150 or 200 mg twice daily (BID). Conclusions: The PBPK model successfully simulated the PK profiles and CDK4/6 occupancy for ABE and its three metabolites in plasma and CSD, and determined the optimal dosing in brain MBC. Overall,The PBPK model can provide important insights for personalized dosing strategies, contributing to improved treatment efficacy and safety for patients, particularly those with brain MBC.

High-throughput kinetics in drug discovery

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

Competition in drug binding and … the race to equilibrium

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

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

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

Radiosynthesis and Preclinical Evaluation of an 18F-Labeled Triazolopyridopyrazine-Based Inhibitor for Neuroimaging of the Phosphodiesterase 2A (PDE2A)

The cyclic nucleotide phosphodiesterase 2A is an intracellular enzyme which hydrolyzes the secondary messengers cAMP and cGMP and therefore plays an important role in signaling cascades. A high expression in distinct brain areas as well as in cancer cells makes PDE2A an interesting therapeutic and diagnostic target for neurodegenerative and neuropsychiatric diseases as well as for cancer. Aiming at specific imaging of this enzyme in the brain with positron emission tomography (PET), a new triazolopyridopyrazine-based derivative (11) was identified as a potent PDE2A inhibitor (IC50, PDE2A = 1.99 nM; IC50, PDE10A ~2000 nM) and has been radiofluorinated for biological evaluation. In vitro autoradiographic studies revealed that [18F]11 binds with high affinity and excellent specificity towards PDE2A in the rat brain. For the PDE2A-rich region nucleus caudate and putamen an apparent KD value of 0.24 nM and an apparent Bmax value of 16 pmol/mg protein were estimated. In vivo PET-MR studies in rats showed a moderate brain uptake of [18F]11 with a highest standardized uptake value (SUV) of 0.97. However, no considerable enrichment in PDE2A-specific regions in comparison to a reference region was detectable (SUVcaudate putamen = 0.51 vs. SUVcerebellum = 0.40 at 15 min p.i.). Furthermore, metabolism studies revealed a considerable uptake of radiometabolites of [18F]11 in the brain (66% parent fraction at 30 min p.i.). Altogether, despite the low specificity and the blood−brain barrier crossing of radiometabolites observed in vivo, [18F]11 is a valuable imaging probe for the in vitro investigation of PDE2A in the brain and has potential as a lead compound for further development of a PDE2A-specific PET ligand for neuroimaging.

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.

Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters

One of the primary cancer treatment modalities is chemotherapy. Unfortunately, traditional anti-cancer drugs are often not selective and cause damage to healthy cells, leading to serious side effects for patients. For this reason more targeted therapeutics and drug delivery methods are being developed. The effectiveness of new treatments is initially determined via in vitro cell viability assays, which determine the IC 50  of the drug. However, these assays are known to result in estimates of IC 50  that depend on the measurement time, possibly resulting in over- or under-estimation of the IC 50 . Here, we test the possibility of using cell growth curves and fitting of mathematical models to determine the IC 50  as well as the maximum efficacy of a drug ( ε max ). We measured cell growth of MCF-7 and HeLa cells in the presence of different concentrations of doxorubicin and fit the data with a logistic growth model that incorporates the effect of the drug. This method leads to measurement time-independent estimates of IC 50  and ε max , but we find that ε max  is not identifiable. Further refinement of this methodology is needed to produce uniquely identifiable parameter estimates.

Synthesis and Biological Evaluation of a Novel 18F-Labeled Radiotracer for PET Imaging of the Adenosine A2A Receptor

The adenosine A_2A receptor (A_2AR) has emerged as a potential non-dopaminergic target for the treatment of Parkinson’s disease and, thus, the non-invasive imaging with positron emission tomography (PET) is of utmost importance to monitor the receptor expression and occupancy during an A_2AR-tailored therapy. Aiming at the development of a PET radiotracer, we herein report the design of a series of novel fluorinated analogs (TOZ1-TOZ7) based on the structure of the A_2AR antagonist tozadenant, and the preclinical evaluation of [¹⁸F]TOZ1. Autoradiography proved A_2AR-specific in vitro binding of [¹⁸F]TOZ1 to striatum of mouse and pig brain. Investigations of the metabolic stability in mice revealed parent fractions of more than 76% and 92% of total activity in plasma and brain samples, respectively. Dynamic PET/magnetic resonance imaging (MRI) studies in mice revealed a brain uptake but no A_2AR-specific in vivo binding.

Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

Does In Vitro Potency Predict Clinically Efficacious Concentrations?

The in vitro affinity of a compound for its target is an important feature in drug discovery, but what remains is how predictive in vitro properties are of in vivo therapeutic drug exposure. We assessed the relationship between in vitro potency and clinically efficacious concentrations for marketed small molecule drugs (n = 164) and how they may differ depending on therapeutic indication, mode of action, receptor type, target localization, and function. Approximately 70% of compounds had a therapeutic unbound plasma exposure lower than in vitro potency; the median ratio of exposure in relation to in vitro potency was 0.32, and 80% had ratios within the range of 0.007 to 8.7. We identified differences in the in vivo–to–in vitro potency ratio between indications, mode of action, target type, and matrix localization, and whether or not the drugs had active metabolites. The in vitro–assay variability contributions appeared to be the smallest; within the same drug target and mode of action the within‐variability was slightly broader; but both were substantially less compared with the overall distribution of ratios. These data suggest that in vitro potency conditions, estimated in vivo potency, required level of receptor occupancy, and target turnover are key components for further understanding the link between clinical drug exposure and in vitro potency.

Drug-Target Association Kinetics in Drug Discovery

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

Binding kinetics of ligands acting at GPCRs

The influence of drug-receptor binding kinetics has often been overlooked during the development of new therapeutics that target G protein-coupled receptors (GPCRs). Over the last decade there has been a growing understanding that an in-depth knowledge of binding kinetics at GPCRs is required to successfully target this class of proteins. Ligand binding to a GPCR is often not a simple single step process with ligand freely diffusing in solution. This review will discuss the experiments and equations that are commonly used to measure binding kinetics and how factors such as allosteric regulation, rebinding and ligand interaction with the plasma membrane may influence these measurements. We will then consider the molecular characteristics of a ligand and if these can be linked to association and dissociation rates.

In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations

Background and Purpose

Target binding kinetics influence the time course of the drug effect (pharmacodynamics) both (i) directly, by affecting the time course of target occupancy, driven by the pharmacokinetics of the drug, competition with endogenous ligands and target turnover, and (ii) indirectly, by affecting signal transduction and homeostatic feedback. For dopamine D₂ receptor antagonists, it has been hypothesized that fast receptor binding kinetics cause fewer side effects, because part of the dynamics of the dopaminergic system is preserved by displacement of these antagonists.

Experimental Approach

Target binding kinetics of D₂ receptor antagonists and signal transduction after dopamine and D₂ receptor antagonist exposure were measured in vitro. These data were integrated by mechanistic modelling, taking into account competitive binding of endogenous dopamine and the antagonist, the turnover of the second messenger cAMP and negative feedback by PDE turnover.

Key Results

The proposed signal transduction model successfully described the cellular cAMP response for 17 D₂ receptor antagonists with widely different binding kinetics. Simulation of the response to fluctuating dopamine concentrations revealed that a significant effect of the target binding kinetics on the dynamics of the signalling only occurs at endogenous dopamine concentration fluctuations with frequencies below 1 min⁻¹.

Conclusions and Implications

Signal transduction and feedback are important determinants of the time course of drug effects. The effect of the D₂ receptor antagonist dissociation rate constant (k_off) is limited to the maximal rate of fluctuations in dopamine signalling as determined by the dopamine k_off and the cAMP turnover.

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

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

Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models

Drug–target binding kinetics (as determined by association and dissociation rate constants, k_on and k_off) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug–target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment–target binding kinetics (EC–TB) model were tested on either plasma (EC_PL, TB_PL and EC–TB_PL) or brain extracellular fluid (ECF) (EC_ECF, TB_ECF and EC–TB_ECF) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (Tmax_TO) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in Tmax_TO (∆Tmax_TO) as a result of increasing dose revealed that ∆Tmax_TO is decreasing towards zero if the k_off is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆Tmax_TO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of k_on and k_off, a target binding model should be considered more often for mechanistic modelling purposes.

Drug-Target Kinetics in Drug Discovery

The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

Distinct in vivo target occupancy by bivalent- and induced-fit-like binding drugs

BACKGROUND AND PURPOSE

Optimal drug therapy often requires long-lasting target occupancy While this attribute was usually linked to the drug's pharmacokinetic properties, the dissociation rate is now increasingly recognized to contribute as well. Nearly all the earlier pharmacokinetic-pharmacodynamic (PK-PD) simulations encompassed single-step binding drugs and focused on koff . However, 'micro'-PK mechanisms and more complex binding mechanisms like bivalent- and induced-fit binding may contribute as well. Corresponding binding models are presently explored.

EXPERIMENTAL APPROACH

We compared the 24 h in vivo occupancy over time profiles of prototype bivalent- and induced-fit-like binding drugs (A and B) after one or repeated daily dosings, both without and with rebinding. Special attention was focused on the effect of each of the microscopic rate constants on the occupancy profiles and on the metrics to represent those profiles.

KEY RESULTS

Although both models can be represented by the same mathematical formulation, drugs A and B display quite different occupancy profiles, even though they have the same potency. These differences can be attributed to the different effects of their microscopic rate constants on their composite koff and also on their susceptibility to experience rebinding. This also affects how the occupancy profiles of bivalent- and induced-fit-like binders progress when repeating the dosings and by changing the dosage.

CONCLUSIONS AND IMPLICATIONS

Closer attention should be paid to more complex binding models in PK-PD simulations. This may help pharmacologists and medicinal chemists to improve the translation of in vitro kinetic measurements from preclinical screening programmes into clinical efficiency.

Biomarkers in epilepsy-A modelling perspective

Biomarkers can be categorised from type 0 (genotype or phenotype), through 6 (clinical scales), each level representing a part of the processes involved in the biological system and drug treatment. This classification facilitates the identification and connection of information required to fully (mathematically) model a disease and its treatment using integrated information from biomarkers. Two recent reviews thoroughly discussed the current status and development of biomarkers for epilepsy, but a path towards the integration of such biomarkers for the personalisation of anti-epileptic drug treatment is lacking. Here we aim to 1) briefly categorise the available epilepsy biomarkers and identify gaps, and 2) provide a modelling perspective on approaches to fill such gaps. There is mainly a lack of biomarker types 2 (target occupancy) and 3 (target activation). Current literature typically focuses on qualitative biomarkers for diagnosis and prediction of treatment response or failure, leaving a need for biomarkers that help to quantitatively understand the overall system to explain and predict differences in disease and treatment outcome. Due to the complexity of epilepsy, filling the biomarker gaps will require collaboration and expertise from the fields of systems biology and systems pharmacology.

Development of a population pharmacokinetic model to predict brain distribution and dopamine D2 receptor occupancy of raclopride in non-anesthetized rat

BACKGROUND

Raclopride is a selective antagonist of the dopamine D2 receptor. It is one of the most frequently used in vivo D2 tracers (at low doses) for assessing drug-induced receptor occupancy (RO) in animals and humans. It is also commonly used as a pharmacological blocker (at high doses) to occupy the available D2 receptors and antagonize the action of dopamine or drugs on D2 in preclinical studies. The aims of this study were to comprehensively evaluate its pharmacokinetic (PK) profiles in different brain compartments and to establish a PK-RO model that could predict the brain distribution and RO of raclopride in the freely moving rat using a LC-MS based approach.

METHODS

Rats (n=24) received a 10-min IV infusion of non-radiolabeled raclopride (1.61μmol/kg, i.e. 0.56mg/kg). Plasma and the brain tissues of striatum (with high density of D2 receptors) and cerebellum (with negligible amount of D2 receptors) were collected. Additional microdialysis experiments were performed in some rats (n=7) to measure the free drug concentration in the extracellular fluid of the striatum and cerebellum. Raclopride concentrations in all samples were analyzed by LC-MS. A population PK-RO model was constructed in NONMEM to describe the concentration-time profiles in the unbound plasma, brain extracellular fluid and brain tissue compartments and to estimate the RO based on raclopride-D2 receptor binding kinetics.

RESULTS

In plasma raclopride showed a rapid distribution phase followed by a slower elimination phase. The striatum tissue concentrations were consistently higher than that of cerebellum tissue throughout the whole experimental period (10-h) due to higher non-specific tissue binding and D2 receptor binding in the striatum. Model-based simulations accurately predicted the literature data on rat plasma PK, brain tissue PK and D2 RO at different time points after intravenous or subcutaneous administration of raclopride at tracer dose (RO <10%), sub-pharmacological dose (RO 10%-30%) and pharmacological dose (RO >30%).

CONCLUSION

For the first time a predictive model that could describe the quantitative in vivo relationship between dose, PK and D2 RO of raclopride in non-anesthetized rat was established. The PK-RO model could facilitate the selection of optimal dose and dosing time when raclopride is used as tracer or as pharmacological blocker in various rat studies. The LC-MS based approach, which doses and quantifies a non-radiolabeled tracer, could be useful in evaluating the systemic disposition and brain kinetics of tracers.

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time

A considerable number of approved drugs show non-equilibrium binding characteristics, emphasizing the potential role of drug residence times for in vivo efficacy. Therefore, a detailed understanding of the kinetics of association and dissociation of a target-ligand complex might provide crucial insight into the molecular mechanism-of-action of a compound. This deeper understanding will help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. In this review, we highlight the contributions of the Kinetics for Drug Discovery (K4DD) Consortium, which targets major open questions related to binding kinetics in an industry-driven public-private partnership.

Unified Software Solution for Efficient SPR Data Analysis in Drug Research

Surface plasmon resonance (SPR) is a powerful method for obtaining detailed molecular interaction parameters. Modern instrumentation with its increased throughput has enabled routine screening by SPR in hit-to-lead and lead optimization programs, and SPR has become a mainstream drug discovery technology. However, the processing and reporting of SPR data in drug discovery are typically performed manually, which is both time-consuming and tedious. Here, we present the workflow concept, design and experiences with a software module relying on a single, browser-based software platform for the processing, analysis, and reporting of SPR data. The efficiency of this concept lies in the immediate availability of end results: data are processed and analyzed upon loading the raw data file, allowing the user to immediately quality control the results. Once completed, the user can automatically report those results to data repositories for corporate access and quickly generate printed reports or documents. The software module has resulted in a very efficient and effective workflow through saved time and improved quality control. We discuss these benefits and show how this process defines a new benchmark in the drug discovery industry for the handling, interpretation, visualization, and sharing of SPR data.

The Added Value of Assessing Ligand-Receptor Binding Kinetics in Drug Discovery

In the past decade drug research community has started to appreciate the indispensable role of ligand-receptor binding kinetics (BK) in drug discovery. Next to the classical equilibrium-based drug evaluation process with affinity and potency values as outcomes, kinetic investigation of the ligand-receptor interaction can aid compound triage in the hit-to-lead campaign and provide additional information to understand the molecular mechanism of drug action. Translational models incorporating BK are emerging as well, which represent powerful tools for the prediction of in vivo effects. In this viewpoint we will summarize some recent findings and discuss and emphasize the added value of ligand-receptor binding kinetics in drug research.

Public-Private Partnerships in Lead Discovery: Overview and Case Studies

The pharmaceutical industry is faced with significant challenges in its efforts to discover new drugs that address unmet medical needs. Safety concerns and lack of efficacy are the two main technical reasons for attrition. Improved early research tools including predictive in silico, in vitro, and in vivo models, as well as a deeper understanding of the disease biology, therefore have the potential to improve success rates. The combination of internal activities with external collaborations in line with the interests and needs of all partners is a successful approach to foster innovation and to meet the challenges. Collaboration can take place in different ways, depending on the requirements of the participants. In this review, the value of public-private partnership approaches will be discussed, using examples from the Innovative Medicines Initiative (IMI). These examples describe consortia approaches to develop tools and processes for improving target identification and validation, as well as lead identification and optimization. The project "Kinetics for Drug Discovery" (K4DD), focusing on the adoption of drug-target binding kinetics analysis in the drug discovery decision-making process, is described in more detail.