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.

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.

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: 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.

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 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.

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.

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.

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.

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.

Bridging the gap between in silico and cell-based analysis of the nuclear factor-kappaB signaling pathway by in vitro studies of IKK2.

Previously, we have shown by sensitivity analysis, that the oscillatory behavior of nuclear factor (NF-kappaB) is coupled to free IkappaB kinase-2 (IKK2) and IkappaBalpha(IkappaBalpha), and that the phosphorylation of IkappaBalpha by IKK influences the amplitude of NF-kappaB oscillations. We have performed further analyses of the behavior of NF-kappaB and its signal transduction network to understand the dynamics of this system. A time lapse study of NF-kappaB translocation in 10,000 cells showed discernible oscillations in levels of nuclear NF-kappaB amongst cells when stimulated with interleukin (IL-1alpha), which suggests a small degree of synchronization amongst the cell population. When the kinetics for the phosphorylation of IkappaBalpha by IKK were measured, we found that the values for the affinity and catalytic efficiency of IKK2 for IkappaBalpha were dependent on assay conditions. The application of these kinetic parameters in our computational model of the NF-kappaB pathway resulted in significant differences in the oscillatory patterns of NF-kappaB depending on the rate constant value used. Hence, interpretation of in silico models should be made in the context of this uncertainty.

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.

Improving the design and analysis of high-throughput screening technology comparison experiments using statistical modeling.

Contemporary small-molecule drug discovery frequently involves the screening of large compound files as a core activity. Subsequently cost, speed, and safety become critical issues. In order to meet this need, numerous technologies have been developed to allow mix and measure approaches, facilitate miniaturization, and to increase speed and to minimize the use of potentially hazardous reagents such as radioactive materials. However, despite the on-paper advantages of these new technologies, risks can remain undefined. For example, the question of whether the novel method will facilitate identification of active chemical series in a way that is comparable with conventional methods arises. In order to address this question, we have taken the approach of carrying out experiments to directly compare the output of high-throughput screens using a given novel approach and a traditional method. The concordance between the screening methods can then be determined via comparison of the numbers and structures of the active molecules identified. This article describes the approach taken in our laboratory to minimize variability in such experiments and shows data that exemplifies the general result of lower than expected concordance. Statistical modeling was subsequently used to facilitate this interpretation. The model used beta-distribution function to generate a real-activity frequency relationship with added normal random error and occasional outliers to represent assay variability. Hence, the effect of assay parameters such as the threshold, the number of real actives, and the number of outliers and the standard deviation could readily be explored. The model was found to describe the data reasonably and moreover was found to be of great utility when it came to planning further optimal experiments. A key conclusion from the model was that concordance between screening methods could appear poor even when one approach is compared with itself. This occurs simply because the result is a function of assay threshold, standard deviation and the true compound % activity. In response to this finding we have adopted alternative experimental designs that more reliably measure the concordance between screening methods.

NanoStore: a concept for logistical improvements of compound handling in high-throughput screening.

Small molecule screening, the systematic encounter of biology space with chemical space, has provoked the emergence of a whole industry that recreates itself by constant iterative improvements to this process. The authors describe an approach to tackle the problem for one of the most time-consuming steps in the execution of a screening campaign, namely, the reformatting of high-throughput screening test compounds from master plates to daughter assay plates used in the execution of the screen. Through an engineered storage procedure, they prepare plates ahead of the screening process with the respective compounds in a ready-to-use format. They show the biological inertness of the method and how it facilitates efficient recovery of compound activity. This uncoupling of normally interconnected processes provides time and compound savings, avoids repeated freeze-thaw cycles of compound solutions, and removes the problems associated with the DMSO sensitivity of certain assays types.

Development of a 1-microl scale assay for mitogen-activated kinase kinase 7 using 2-D fluorescence intensity distribution analysis anisotropy.

This paper describes the development of a robust, miniaturizable, and quantitative fluorescence-based assay for mitogen-activated protein kinase kinase 7 (MKK7). As a first step, the basic steady-state kinetics of the MKK7-catalyzed phosphorylation of c-Jun N-terminal kinases (JNKs) 1, 2, and 3 were defined using standard radiometric methods. Subsequently, the authors found that in addition to the holo JNKs, a series of novel small peptides (based on the region around the JNK phosphorylation site) are also substrates, provided that these were prephosphorylated on the Y residue of the TPY motif. One of these peptide substrates was used in the development of a fluorescence polarization-based assay using an antibody as a sensor. The assay was successfully miniaturized for use with conventional fluorescence polarization (FP) reader technology in 8.5 microl and on the single microl scale using Evotec proprietary 2-dimensional fluorescence intensity distribution analysis (2D-FIDA) anisotropy and liquid handling technology. The steady-state kinetic parameters derived using the FP or 2D-FIDA anisotropy format assays correlated well with those generated using a radiometric assay. Moreover, the quantitative sensitivity to known inhibitors was maintained independent of the format and assay volume. In addition, the authors found that the 2D-FIDA anisotropy assay exhibited superior performance statistics (typical Z' = approximately 0.5) relative to conventional FP (typical Z' = 0.3) and yielded the additional benefit of order-of-magnitude savings in terms of reagent costs. The 2D-FIDA anisotropy assay was used to carry out a successful high-throughput screening in 1-microl final volume against company file compounds.