Ch. Schütte

Quantifying the Impact of Nevirapine-Based Prophylaxis Strategies To Prevent Mother-to-Child Transmission of HIV-1: a Combined Pharmacokinetic, Pharmacodynamic, and Viral Dynamic Analysis To Predict Clinical Outcomes

ABSTRACT Single-dose nevirapine (sd-NVP) and extended NVP prophylaxis are widely used in resource-constrained settings to prevent vertical HIV-1 transmission. We assessed the pharmacokinetics of sd-NVP in 62 HIV-1-positive pregnant Ugandan woman and their newborns who were receiving sd-NVP prophylaxis to prevent mother-to-child HIV-1 transmission. Based on these data, we developed a mathematical model system to quantify the impact of different sd-NVP regimens at delivery and of extended infant NVP prophylaxis (6, 14, 21, 26, 52, 78, and 102 weeks) on the 2-year risk of HIV-1 transmission and development of drug resistance in mothers and their breast-fed infants. Pharmacokinetic parameter estimates and model-predicted HIV-1 transmission rates were very consistent with other studies. Predicted 2-year HIV-1 transmission risks were 35.8% without prophylaxis, 31.6% for newborn sd-NVP, 19.1% for maternal sd-NVP, and 19.7% for maternal/newborn sd-NVP. Maternal sd-NVP reduced newborn infection predominately by transplacental exchange, providing protective NVP concentrations to the newborn at delivery, rather than by maternal viral load reduction. Drug resistance was frequently selected in HIV-1-positive mothers after maternal sd-NVP. Extended newborn NVP prophylaxis further decreased HIV-1 transmission risks, but an overall decline in cost-effectiveness for increasing durations of newborn prophylaxis was indicated. The total number of infections with resistant virus in newborns was not increased by extended newborn NVP prophylaxis. The developed mathematical modeling framework successfully predicted the risk of HIV-1 transmission and resistance development and can be adapted to other drugs/drug combinations to a priori assess their potential in reducing vertical HIV-1 transmission and resistance spread.

Multiscale modelling in molecular dynamics : Biomolecular conformations as metastable states

We report on a novel approach to the automatic identification of metastable states from long term simulation of complex molecular systems. The new approach is based on a hierarchical concept of metastability: metastable states are understood as subsets of state or configuration space from which the dynamics exits only very rarely; subsets with the smallest exit probabilities are of most interest, their further decomposition then may reveal subsets from which exiting is less but comparably difficult for the system under investigation. The article gives a survey of the theoretical foundation of the approach and its algorithmic realization that generalizes the well-known concept of Hidden Markov Models. The performance of the resulting algorithm are illustrated by application to a 100 ns simulation of penta-alanine with explicit water. We demonstrate the resulting metastable states allow to reveal the conformation dynamics of the moelcule.

Statistical parameter estimation and signal classification in cardiovascular diagnosis

Medical technology has seen impressive success in the past decades, generating novel clinical data at an unexpected rate. Even though numerous physiological models have been developed, their clinical application is limited. The major reason for this lies in the difficulty of finding and interpreting the model parameters, because most problems are ill-posed and do not have unique solutions. On the one hand the reason for this lies in the information deficit of the data, which is the result of finite measurement precision and contamination by artifacts and noise and on the other hand on data mining procedures that cannot sufficiently treat the statistical nature of the data. Within this work we introduce a population based parameter estimation method that is able to reveal structural parameters that can be used for patient-specific modeling. In contrast to traditional approaches this method produces a distribution of physiologically interpretable models defined by patient-specific parameters and model states. On the basis of these models we identify disease specific classes that correspond to clinical diagnoses, which enable a probabilistic assessment of human health condition on the basis of a broad patient population. In an ongoing work this technique is used to identify arterial stenosis and aneurisms from anomalous patterns in parameter space. We think that the information-based approach will provide a useful link between mathematical models and clinical diagnoses and that it will become a constituent in medicine in near future.

Metastability of Diffusion Processes

Diffusion models arising in analysis of real world systems are typically far too complex for exact solution, or even meaningful simulation. The purpose of this paper is to develop foundations for model reduction, and new modeling tech niques for diffusion models. Based on the main assumption of V-uniform ergodicity of the diffusion process it is shown that real eigenfunctions provide a decomposition of the state space into so-called metastable sets. We give a novel definition of metastability via exit rates which seems to be promising for a algorithmic identification of metastable sets even for large scale systems.

In silico cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurements

Early and reliable identification of chemical toxicity is of utmost importance. At the same time, reduction of animal testing is paramount. Therefore, methods that improve the interpretability and usability of in vitro assays are essential. xCELLigences real-time cell analyzer (RTCA) provides a novel, fast and cost effective in vitro method to probe compound toxicity. We developed a simple mathematical framework for the qualitative and quantitative assessment of toxicity for RTCA measurements. Compound toxicity, in terms of its 50% inhibitory concentration IC50 on cell growth, and parameters related to cell turnover were estimated on cultured IEC-6 cells exposed to 10 chemicals at varying concentrations. Our method estimated IC50 values of 113.05, 7.16, 28.69 and 725.15 μM for the apparently toxic compounds 2-acetylamino-fluorene, aflatoxin B1, benzo-[a]-pyrene and chloramphenicol in the tested cell line, in agreement with literature knowledge. IC50 values of all apparent in vivo non-toxic compounds were estimated to be non-toxic by our method. Corresponding estimates from RTCAs in-built model gave false positive (toxicity) predictions in 5/10 cases. Taken together, our proposed method reduces false positive predictions and reliably identifies chemical toxicity based on impedance measurements. The source code for the developed method including instructions is available at https://git.zib.de/bzfgupta/toxfit/tree/master.