No abstract

Due to its high early bactericidal activity, isoniazid (INH) plays an essential role in tuberculosis treatment. Genetic polymorphisms of N-acetyltransferase type 2 (NAT2) cause a trimodal distribution of INH pharmacokinetics in slow, intermediate, and fast acetylators. The success of INH-based chemotherapy is associated with acetylator and patient health status. Still, a standard dose recommended by the FDA is administered regardless of acetylator type or immune status, even though adverse effects occur in 5 to 33% of all patients. Slow acetylators have a higher risk of development of drug-induced toxicity, while fast acetylators and immune-deficient patients face lower treatment success rates. To mechanistically assess the trade-off between toxicity and efficacy, we developed a physiologically based pharmacokinetic (PBPK) model describing the NAT2-dependent pharmacokinetics of INH and its metabolites. We combined the PBPK model with a pharmacodynamic (PD) model of antimycobacterial drug effects in the lungs. The resulting PBPK/PD model allowed the simultaneous simulation of treatment efficacies at the site of infection and exposure to toxic metabolites in off-target organs. Subsequently, we evaluated various INH dosing regimens in NAT2-specific immunocompetent and immune-deficient virtual populations. Our results suggest the need for acetylator-specific dose adjustments for optimal treatment outcomes. A reduced dose for slow acetylators substantially lowers the exposure to toxic metabolites and thereby the risk of adverse events, while it maintains sufficient treatment efficacies. Vice versa, intermediate and fast acetylators benefit from increased INH doses and a switch to a twice-daily administration schedule. Our analysis outlines how PBPK/PD modeling may be used to design and individualize treatment regimens.

No abstract

Abstract. The discrete logarithm problem in various finite abelian groups is the basis for some well known public key cryptosystems. Recently, real quadratic congruence function fields were used to construct a public key distribution system. The security of this public key system is based on the difficulty of a discrete logarithm problem in these fields. In this paper, we present a probabilistic algorithm with subexponential running time that computes such discrete logarithms in real quadratic congruence function fields of sufficiently large genus. This algorithm is a generalization of similar algorithms for real quadratic number fields.

Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1723-x) contains supplementary material, which is available to authorized users.

Drug-induced liver injury (DILI) is a matter of concern in the course of drug development and patient safety, often leading to discontinuation of drug-development programs or early withdrawal of drugs from market. Hepatocellular toxicity or impairment of bile acid (BA) metabolism, known as cholestasis, are the two clinical forms of DILI. Whole-body physiology-based modelling allows a mechanistic investigation of the physiological processes leading to cholestasis in man. Objectives of the present study were: (1) the development of a physiology-based model of the human BA metabolism, (2) population-based model validation and characterisation, and (3) the prediction and quantification of altered BA levels in special genotype subgroups and after drug administration. The developed physiology-based bile acid (PBBA) model describes the systemic BA circulation in humans and includes mechanistically relevant active and passive processes such as the hepatic synthesis, gallbladder emptying, transition through the gastrointestinal tract, reabsorption into the liver, distribution within the whole body, and excretion via urine and faeces. The kinetics of active processes were determined for the exemplary BA glycochenodeoxycholic acid (GCDCA) based on blood plasma concentration-time profiles. The robustness of our PBBA model was verified with population simulations of healthy individuals. In addition to plasma levels, the possibility to estimate BA concentrations in relevant tissues like the intracellular space of the liver enhance the mechanistic understanding of cholestasis. We analysed BA levels in various tissues of Benign Recurrent Intrahepatic Cholestasis type 2 (BRIC2) patients and our simulations suggest a higher susceptibility of BRIC2 patients toward cholestatic DILI due to BA accumulation in the liver. The effect of drugs on systemic BA levels were simulated for cyclosporine A (CsA). Our results confirmed the higher risk of DILI after CsA administration in healthy and BRIC2 patients. The presented PBBA model enhances our mechanistic understanding underlying cholestasis and drug-induced alterations of BA levels in blood and organs. The developed PBBA model might be applied in the future to anticipate potential risk of cholestasis in patients.

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data.

Abstract.We generalize and improve t h e schemes of [4]. We introduce analogues of exponentiation and cliscrele logarithms in the principle cycle of real quadratic orders. This enables us t o implement many cryptographic protocols based on discrete logarithms, e.g. a variant of t h e signature scheme of ElGamal [S].