First-in-human phase I study of the bromodomain and extraterminal motif inhibitor BAY 1238097: emerging pharmacokinetic/pharmacodynamic relationship and early termination due to unexpected toxicity.

BACKGROUND: Bromodomain and extraterminal motif (BET) protein inhibition is a promising cancer treatment strategy, notably for targeting MYC- or BRD4-driven diseases. A first-in-human study investigated the safety, pharmacokinetics, maximum tolerated dose and recommended phase II dose of the BET inhibitor BAY 1238097 in patients with advanced malignancies. MATERIAL AND METHODS: In this phase I, open-label, non-randomised, multicentre study, patients with cytologically or histologically confirmed advanced refractory malignancies received oral BAY 1238097 twice weekly in 21-day cycles using an adaptive dose-escalation design at a starting dose of 10 mg/week. Model-based dose-response analysis was performed to guide dose escalation. Safety, pharmacokinetics, pharmacodynamics and tumour response were evaluated. RESULTS: Eight patients were enrolled at three dose levels (10 mg/week, n = 3; 40 mg/week, n = 3; 80 mg/week, n = 2). Both patients receiving 80 mg/week had dose-limiting toxicities (DLTs) (grade 3 vomiting, grade 3 headache and grade 2/3 back pain). The most common adverse events were nausea, vomiting, headache, back pain and fatigue. Pharmacokinetic analysis indicated a linear dose response with increasing dose. Two patients displayed prolonged stable disease; no responses were observed. Biomarker evaluation of MYC and HEXIM1 expression demonstrated an emerging pharmacokinetic/pharmacodynamic relationship, with a trend towards decreased MYC and increased HEXIM1 expression in response to treatment. CONCLUSION: The study was prematurely terminated because of the occurrence of DLTs at a dose below targeted drug exposure. Pharmacokinetic modelling indicated that an alternate dosing schedule whereby DLTs could be avoided while reaching efficacious exposure was not feasible. Registration number: NCT02369029.

Quantitative Systems Pharmacology Model for Alzheimer Disease Indicates Targeting Sphingolipid Dysregulation as Potential Treatment Option.

Alzheimer disease (AD) is a devastating neurodegenerative disorder with high unmet medical need. Drug development is hampered by limited understanding of the disease and its driving factors. Quantitative Systems Pharmacology (QSP) modeling provides a comprehensive quantitative framework to evaluate the relevance of biological mechanisms in the context of disease and to predict the efficacy of novel treatments. Here, we report a QSP model for AD with a particular focus on investigating the relevance of dysregulation of cholesterol and sphingolipids. We show that our model captures the modulation of several biomarkers in subjects with AD, as well as the response to pharmacological interventions. We evaluate the impact of targeting the sphingosine-1-phosphate 5 receptor (S1PR5) as a potential novel treatment option for AD, and model predictions increase our confidence in this novel disease pathway. Future applications for the QSP model are in validation of further targets and identification of potential treatment response biomarkers.

Use of mathematics to guide target selection in systems pharmacology; application to receptor tyrosine kinase (RTK) pathways.

A key element of the drug discovery process is target selection. Although the topic is subject to much discussion and experimental effort, there are no defined quantitative rules around optimal selection. Often 'rules of thumb', that have not been subject to rigorous exploration, are used. In this paper we explore the 'rule of thumb' notion that the molecule that initiates a pathway signal is the optimal target. Given the multi-factorial and complex nature of this question, we have simplified an example pathway to its logical minimum of two steps and used a mathematical model of this to explore the different options in the context of typical small and large molecule drugs. In this paper, we report the conclusions of our analysis and describe the analysis tool and methods used. These provide a platform to enable a more extensive enquiry into this important topic.

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way.

Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis.

Mathematical models have been widely used for understanding the dynamics of the hepatitis C virus (HCV). We propose a method to predict final clinical outcome for 24 HIV-HCV - coinfected patients with the help of a mathematical model based on the first two weeks of PEG-IFN therapy. Applying a pharmacokinetic-pharmacodynamic (PKPD) approach, together with mixture models, to the adapted model of viral dynamics developed by Neumann et al., we have analyzed the influence of PEG-IFN on the kinetics and interaction of target cells, infected cells and virus mRNA. It was found that PEG-IFN pharmacokinetic parameters were similar in sustained virological responders and nonresponders, while the plasma PEG-IFN concentration that decreases HCV production by 50% (EC50) and the rate of infected cell death were different. The treatment outcome depended mainly on the initial viral mRNA concentration and the rate of infected cell death. The population PKPD approach with a mixture model enabled the determination of individual PKPD parameters and showed high sensitivity (93.5%) and specificity (97.4%) for the prediction of the treatment outcome.

Modeling of celiac disease immune response and the therapeutic effect of potential drugs.

BACKGROUND: Celiac disease (CD) is an autoimmune disorder that occurs in genetically predisposed people and is caused by a reaction to the gluten protein found in wheat, which leads to intestinal villous atrophy. Currently there is no drug for treatment of CD. The only known treatment is lifelong gluten-free diet. The main aim of this work is to develop a mathematical model of the immune response in CD patients and to predict the efficacy of a transglutaminase-2 (TG-2) inhibitor as a potential drug for treatment of CD. RESULTS: A thorough analysis of the developed model provided the following results:1. TG-2 inhibitor treatment leads to insignificant decrease in antibody levels, and hence remains higher than in healthy individuals.2. TG-2 inhibitor treatment does not lead to any significant increase in villous area.3. The model predicts that the most effective treatment of CD would be the use of gluten peptide analogs that antagonize the binding of immunogenic gluten peptides to APC. The model predicts that the treatment of CD by such gluten peptide analogs can lead to a decrease in antibody levels to those of normal healthy people, and to a significant increase in villous area. CONCLUSIONS: The developed mathematical model of immune response in CD allows prediction of the efficacy of TG-2 inhibitors and other possible drugs for the treatment of CD: their influence on the intestinal villous area and on the antibody levels. The model also allows to understand what processes in the immune response have the strongest influence on the efficacy of different drugs. This model could be applied in the pharmaceutical R&D arena for the design of drugs against autoimmune small intestine disorders and on the design of their corresponding clinical trials.

Cross-membrane signal transduction of receptor tyrosine kinases (RTKs): from systems biology to systems pharmacology.

Receptor tyrosine kinases are high-affinity cell surface receptors for many polypeptide growth factors, cytokines, and hormones. They straddle the cell wall and play an important role in cross-membrane signalling. We present a two-component systems pharmacology model based on the local physiology and identify characteristic features of its dynamics. We thus present a transparent tool for studying the effects of drug intervention and ways of administration on cross-membrane signalling through these receptors.

Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology.

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.

Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development.

Mechanistic PKPD models are now advocated not only by academic and industrial researchers, but also by regulators. A recent development in this area is based on the growing realisation that innovation could be dramatically catalysed by creating synergy at the interface between Systems Biology and PKPD, two disciplines which until now have largely existed in 'parallel universes' with a limited track record of impactful collaboration. This has led to the emergence of systems pharmacology. Broadly speaking, this is the quantitative analysis of the dynamic interactions between drug(s) and a biological system to understand the behaviour of the system as a whole, as opposed to the behaviour of its individual constituents; thus, it has become the interface between PKPD and systems biology. It applies the concepts of Systems Engineering, Systems Biology, and PKPD to the study of complex biological systems through iteration between computational and/or mathematical modelling and experimentation. Application of systems pharmacology can now impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies, and has the potential to become an integral component of a new 'enhanced quantitative drug discovery and development' (EQD3) R&D paradigm.

Design and optimisation of orally active TLR7 agonists for the treatment of hepatitis C virus infection.

The synthesis and structure-activity relationships of a series of novel interferon inducers are described. Pharmacokinetic studies and efficacy assessment of a series of 8-oxo-3-deazapurine analogues led to the identification of compound 33, a potent and selective agonist of the TLR7 receptor with an excellent in vivo efficacy profile in a mouse model.

Impact of protein binding on receptor occupancy: a two-compartment model.

In this paper we analyse the impact of protein-, lipid- and receptor-binding on receptor occupancy in a two-compartment system, with proteins in both compartments and lipids and receptors in the peripheral compartment only. We do this for two manners of drug administration: a bolus administration and a constant rate infusion, both into the central compartment. We derive explicit approximations for the time-curves of the different compounds valid for a wide range of realistic values of rate constants and initial concentrations of proteins, lipids, receptors and the drug. These approximations are used to obtain both qualitative and quantitative insight into such critical properties as the distribution of the drug over the two compartments, the maximum receptor occupancy and the area under the drug-receptor complex curve. In particular we focus on assessing the impact of the dissociation constants, K(P), K(L) and K(R) of the drug with, respectively, the proteins, the lipids and the receptors, the permeability and the surface area of the membrane between compartments, and the rate the drug is eliminated from the system.

Translational pharmacokinetic-pharmacodynamic modelling; application to cardiovascular safety data for PF-00821385, a novel HIV agent.

AIM: To assess the translation of pharmacokinetic-pharmacodynamic (PK-PD) relationships for heart rate effects of PF-00821385 in dog and man. METHODS: Cardiovascular telemetric parameters and concentration data were available for animals receiving active doses (0.5-120 mg kg(-1), n= 4) or vehicle. PF-00821385 was administered to 24 volunteers and pharmacokinetic and vital signs data were collected. PK-PD models were fitted using nonlinear mixed effects. RESULTS: Compartmental models with linear absorption and clearance were used to describe pharmacokinetic disposition in animal and man. Diurnal variation in heart and pulse rate was best described with a single cosine function in both dog and man. Canine and human heart rate change were described by a linear model with free drug slope 1.76 bpm microM(-1)[95% confidence interval (CI) 1.17, 2.35] in the dog and 0.76 bpm microM(-1) (95% CI 0.54, 1.14) in man. CONCLUSIONS: The preclinical translational of concentration-response has been described and the potential for further interspecies extrapolation and optimization of clinical trial design is addressed.

Estimation of binding rate constants using a simultaneous mixed-effects method: application to monoamine transporter reuptake inhibitor reboxetine.

BACKGROUND AND PURPOSE: Reboxetine is a clinically used antidepressant and is a racemic mixture of two enantiomers, SS- and RR-reboxetine. The aim of the work described in this manuscript was to determine the kinetics of binding of the RR- and SS-reboxetine to the human noradrenaline transporter (hNET). EXPERIMENTAL APPROACH: We have applied a simultaneous mixed-effects method to the analysis of the transient kinetics of binding of SS-, RR- and racemic reboxetine to hNET. This method allowed simultaneous modelling of multiple datasets, taking into account inter-experiment variability, thereby facilitating robust parameter estimation and minimizing the assumptions made. KEY RESULTS: The mixed-effects method proved simple and robust. SS-reboxetine bound to hNET according to a one-step binding model with the SS-enantiomer having 130-fold higher steady state affinity than the RR-enantiomer (K(d)= 0.076 +/- 0.009 nM vs. 9.7 +/- 0.8 nM respectively). The k(on) for SS-reboxetine was c. 1.4 x 10(5) M(-1).s(-1) and k(off) 1.05 x 10(-5) s(-1) (t(1/2) approximately 18 h). The k(on) for RR-reboxetine was c. 4.3 x 10(5) M(-1).s(-1) and k(off) 4.2 x 10(-3) s(-1) (t(1/2) approximately 3 min). The racemate behaved as expected for an equimolar mixture of RR- and SS-reboxetine, assuming mutually exclusive binding. CONCLUSIONS AND IMPLICATIONS: These data will be useful for the interpretation of the behaviour of reboxetine and its enantiomers in man and the method used could be applied to other candidate drugs.

Pharmacokinetic-pharmacodynamic modeling of alpha interferon response induced by a Toll-like 7 receptor agonist in mice.

Recombinant alpha interferon (IFN-alpha) is used in the treatment of hepatitis C virus (HCV)-infected patients but is not optimal in terms of efficacy or tolerability. Toll-like 7 receptor (TLR-7) agonists stimulate the innate immune system to produce, among other cytokines, IFN-alpha and are being evaluated as alternative drugs to treat HCV infection. This paper describes the application of pharmacokinetic-pharmacodynamic (PK-PD) modeling to understanding the behavior of a TLR-7 agonist [9-benzyl-8-hydroxy-2-(2-methoxyethoxy) adenine (BHMA)] in mice, using IFN-alpha as a biomarker. This is the first report of such a PK-PD model, and the conclusions may be of utility in the clinical development of TLR-7 agonists for HCV infection.

Impact of plasma-protein binding on receptor occupancy: an analytical description.

In this paper we analyse the dynamics of an inhibitor I which can either bind to a receptor R or to a plasma protein P. Assuming typical association and dissociation rates, we find that after an initial dose of inhibitor, there are three time scales: a short one, measured in fractions of seconds, in which the inhibitor concentration and the plasma-protein complex jump to quasi-stationary values, a medium one, measured in seconds in which the receptor complex rises to an equilibrium value and a large one, measured in hours in which the inhibitor-receptor complex slowly drops down to zero. We show that the average receptor occupancy, the pharmacologically relevant quantity, taken over, say, 24h reaches a maximal value for a specific value of the plasma-protein binding constant. Potentially, understanding and exploiting this optimum could be of great interest to those involved in drug discovery and development.

Proximate parameter tuning for biochemical networks with uncertain kinetic parameters.

It is commonly the case in biochemical modelling that we have knowledge of the qualitative 'structure' of a model and some measurements of the time series of the variables of interest (concentrations and fluxes), but little or no knowledge of the model's parameters. This is, then, a system identification problem, that is commonly addressed by running a model with estimated parameters and assessing how far the model's behaviour is from the 'target' behaviour of the variables, and adjusting parameters iteratively until a good fit is achieved. The issue is that most of these problems are grossly underdetermined, such that many combinations of parameters can be used to fit a given set of variables. We introduce the constraint that the estimated parameters should be within given bounds and as close as possible to stated nominal values. This deterministic 'proximate parameter tuning' algorithm turns out to be exceptionally effective, and we illustrate its utility for models of p38 signalling, of yeast glycolysis and for a benchmark dataset describing the thermal isomerisation of alpha-pinene.