Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.

Ab initio modeling and molecular dynamics simulation of the alpha 1b-adrenergic receptor activation.

This work describes the ab initio procedure employed to build an activation model for the alpha 1b-adrenergic receptor (alpha 1b-AR). The first version of the model was progressively modified and complicated by means of a many-step iterative procedure characterized by the employment of experimental validations of the model in each upgrading step. A combined simulated (molecular dynamics) and experimental mutagenesis approach was used to determine the structural and dynamic features characterizing the inactive and active states of alpha 1b-AR. The latest version of the model has been successfully challenged with respect to its ability to interpret and predict the functional properties of a large number of mutants. The iterative approach employed to describe alpha 1b-AR activation in terms of molecular structure and dynamics allows further complications of the model to allow prediction and interpretation of an ever-increasing number of experimental data.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Expanding molecular modeling and design tools to non-natural sidechains.

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.

Emerging environmental and weather challenges in outdoor sports

Given the climatic changes around the world and the growing outdoor sports participation, existing guidelines and recommendations for exercising in naturally challenging environments such as heat, cold or altitude, exhibit potential shortcomings. Continuous efforts from sport sciences and exercise physiology communities aim at minimizing the risks of environmental-related illnesses during outdoor sports practices. Despite this, the use of simple weather indices does not permit an accurate estimation of the likelihood of facing thermal illnesses. This provides a critical foundation to modify available human comfort modeling and to integrate bio-meteorological data in order to improve the current guidelines. Although it requires further refinement, there is no doubt that standardizing the recently developed Universal Thermal Climate Index approach and its application in the field of sport sciences and exercise physiology may help to improve the appropriateness of the current guidelines for outdoor, recreational and competitive sports participation. This review first summarizes the main environmental-related risk factors that are susceptible to increase with recent climate changes when exercising outside and offers recommendations to combat them appropriately. Secondly, we briefly address the recent development of thermal stress models to assess the thermal comfort and physiological responses when practicing outdoor activities in challenging environments.

Microsatellites can be misleading: an empirical and simulation study.

It has been long recognized that highly polymorphic genetic markers can lead to underestimation of divergence between populations when migration is low. Microsatellite loci, which are characterized by extremely high mutation rates, are particularly likely to be affected. Here, we report genetic differentiation estimates in a contact zone between two chromosome races of the common shrew (Sorex araneus), based on 10 autosomal microsatellites, a newly developed Y-chromosome microsatellite, and mitochondrial DNA. These results are compared to previous data on proteins and karyotypes. Estimates of genetic differentiation based on F- and R-statistics are much lower for autosomal microsatellites than for all other genetic markers. We show by simulations that this discrepancy stems mainly from the high mutation rate of microsatellite markers for F-statistics and from deviations from a single-step mutation model for R-statistics. The sex-linked genetic markers show that all gene exchange between races is mediated by females. The absence of male-mediated gene flow most likely results from male hybrid sterility.

International Olympic Committee consensus statement on thermoregulatory and altitude challenges for high-level athletes

Challenging environmental conditions, including heat and humidity, cold, and altitude, pose particular risks to the health of Olympic and other high-level athletes. As a further commitment to athlete safety, the International Olympic Committee (IOC) Medical Commission convened a panel of experts to review the scientific evidence base, reach consensus, and underscore practical safety guidelines and new research priorities regarding the unique environmental challenges Olympic and other international-level athletes face. For non-aquatic events, external thermal load is dependent on ambient temperature, humidity, wind speed and solar radiation, while clothing and protective gear can measurably increase thermal strain and prompt premature fatigue. In swimmers, body heat loss is the direct result of convection at a rate that is proportional to the effective water velocity around the swimmer and the temperature difference between the skin and the water. Other cold exposure and conditions, such as during Alpine skiing, biathlon and other sliding sports, facilitate body heat transfer to the environment, potentially leading to hypothermia and/or frostbite; although metabolic heat production during these activities usually increases well above the rate of body heat loss, and protective clothing and limited exposure time in certain events reduces these clinical risks as well. Most athletic events are held at altitudes that pose little to no health risks; and training exposures are typically brief and well-tolerated. While these and other environment-related threats to performance and safety can be lessened or averted by implementing a variety of individual and event preventative measures, more research and evidence-based guidelines and recommendations are needed. In the mean time, the IOC Medical Commission and International Sport Federations have implemented new guidelines and taken additional steps to mitigate risk even further.

Big data and other challenges in the quest for orthologs.

Given the rapid increase of species with a sequenced genome, the need to identify orthologous genes between them has emerged as a central bioinformatics task. Many different methods exist for orthology detection, which makes it difficult to decide which one to choose for a particular application. Here, we review the latest developments and issues in the orthology field, and summarize the most recent results reported at the third 'Quest for Orthologs' meeting. We focus on community efforts such as the adoption of reference proteomes, standard file formats and benchmarking. Progress in these areas is good, and they are already beneficial to both orthology consumers and providers. However, a major current issue is that the massive increase in complete proteomes poses computational challenges to many of the ortholog database providers, as most orthology inference algorithms scale at least quadratically with the number of proteomes. The Quest for Orthologs consortium is an open community with a number of working groups that join efforts to enhance various aspects of orthology analysis, such as defining standard formats and datasets, documenting community resources and benchmarking. AVAILABILITY AND IMPLEMENTATION: All such materials are available at http://questfororthologs.org. CONTACT: erik.sonnhammer@scilifelab.se or c.dessimoz@ucl.ac.uk.

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (congruent with 73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r (2) = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pK(m) values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.