Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics.

Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientic community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identication of the microscopic pharmacokinetics and pharmacodynamics of a drug with a minimal a priori knowledge. In fact, the inference model presented in this paper is completely unsupervised. It takes as input the time series of the concetrations of the parent drug and its metabolites. The method, applied to the case study of the gemcitabine pharmacokinetics, shows good accuracy and sensitivit

Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy

<div><p>Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.</p> </div

Pharmacokinetics and pharmacodynamics of natalizumab in pediatric patients with RRMS

ObjectiveThis phase I study investigated pharmacokinetic (PK) and pharmacodynamic (PD) profiles of natalizumab in pediatric patients with relapsing-remitting MS (RRMS).MethodsPediatric patients with RRMS who were prescribed natalizumab 300 mg IV every 4 weeks were enrolled. Blood samples were collected on days 1, 2, 8, 15, and 22 and at weeks 4, 8, 12, and 16 to estimate PK parameters; PD properties were evaluated by measuring \u3b14-integrin saturation and lymphocyte counts over time. Natalizumab's safety profile was also evaluated.ResultsPK parameters were similar to those reported in adult patients; natalizumab concentrations peaked approximately 1 day after infusion in most of the participants (Cmax 142.9 g/mL, AUClast 47389.4 hrg/mL), followed by a biphasic decline with a rapid distribution phase and a slow elimination phase, with a terminal half-life of 215.1 hours. In terms of PD, both time course and magnitude of \u3b14-integrin saturation and increase in lymphocyte counts were similar to those observed in adults. During the 16-week study follow-up, 3 adverse events attributed to natalizumab were observed; no unexpected safety events occurred.ConclusionsPK profile, \u3b14-integrin saturation, lymphocyte counts, and safety observed in these pediatric patients are comparable to those reported in adults.Classification of evidenceThis study provides Class I evidence that natalizumab PK/PD parameters and safety profile are similar in adults and pediatric patients in the short term. Longer studies, also including a larger number of younger subjects (aged 10-12 years), are required to further inform about long-term PK and PD parameters in pediatric patients with MS

The Societal Impact Of Natalizumab Treatment In The Italian Relapsing-Remitting Multiple Sclerosis Clinical Practice: The Tysabri® Pharmacoeconomics (Type) Study

Objectives: Recently, long-term treatment effects of GA and interferons-ß (IFNs) observed in the UK multiple sclerosis Risk Sharing Scheme (RSS) have become available. Using a UK National Health Service perspective, we evaluated the costeffectiveness of GA for RRMS using either randomised controlled trial (RCT) or RSS data. Methods: A discrete Markov model comparing GA (20 mg qd or 40 mg tiw) to BSC, IFN-1a 44µg, IFN-1a 22µg, IFN-1a 30µg and IFN-1b was developed. The model has 21 health states defined by Expanded Disability Status Scale and a 50-year time horizon. It also incorporates adverse events, treatment discontinuation, various second-line treatments and neutralising antibodies. Relapse rates and disability progression transition probabilities were obtained from natural history studies in RRMS patients. Treatment effects were informed by a de novo network metaanalysis using results of RCTs and outputs were compared to a scenario in which real-world data from the RSS was used. Univariate and probabilistic sensitivity analyses were performed. Results: GA dominated IFN-1a 22µg and IFN-1a 30µg as it represented lower overall costs and QALY gains of 0.226 and 0.067, respectively. GA is cost-effective compared to BSC at an incremental cost-effectiveness ratio (ICER) of £14,789 per QALY gained. Albeit a reversed ICER was observed, GA remained cost-effective against IFN-1a 44µg and IFN-1b. The model was most sensitive to the treatment-specific hazard ratio (HR) for disability progression. Other influential factors included the proportion of patients switching to second-line treatments, discount rates, treatment waning and health-state costs. Scenario analyses using treatment-specific HR from the RSS confirmed the robustness of the base case findings and compared well with the 6-year RSS results. Conclusions: Different modelling approaches and data sources consistently show that GA is a cost-effective option for treating RRMS. GA was shown to be more effective than predicted at the outset of the RS

The societal impact of treatment with natalizumab of relapsing–remitting multiple sclerosis in Italian clinical practice: The Tysabri® PharmacoEconomics (TyPE) Study

Multiple sclerosis progressively impairs patients’ ability to independently perform activities of daily living, reduces working capacity, and negatively affects social interactions and relationships, imposing a cost for the society. The aim of this study was to explore the impact on society of treatment of multiple sclerosis with natalizumab in Italian clinical practice. A prospective, observational study was conducted in 24 specialized centers throughout Italy. Direct and indirect costs, as well as the health-related quality of life of patients undergoing treatments, were estimated, while societal impacts were determined using a cost-utility approach. Non-medical direct and indirect costs accounted for 55.7% of the total cost prior to treatment and up to 47.5% after treatment with natalizumab. From the social perspective, greater medical direct costs for second-line drug treatment are nearly offset by savings on non-medical direct costs and by greater productivity, resulting in a +0.5% incremental cost. Assuming a societal perspective, the first year of treatment with natalizumab in real-world clinical practice results in an incremental cost of €2814.8/qualityadjusted life year gained. Multiple sclerosis imposes a considerable burden on patients, their families, and caregivers, stressing the importance of considering the societal perspective in the appraisal process. Treatment with natalizumab shows a noteworthy benefit in social terms