Modeling air-to-air communication networks in the North Atlantic region.

The North Atlantic is one of the world's airspaces accommodating a very high aircraft density while at the same time no radio coverage or radar surveillance is available. Beside satellite communication, one approach to enable data communication between aircraft and ground in the North Atlantic region is to establish ad-hoc  networks build up by direct data links between the aircraft that are acting as communication nodes. In this paper we, therefore, present a modeling approach to model air traffic and ad-hoc networks in the North Atlantic region using up-to-date flight plans and trajectory modeling techniques and to assess the connectivity provided by such networks. Assuming an applicable set of ground stations that provide data transfer to and from this airborne network, we assess the connectivity by time-series analysis and in total for a set of different fractions of all aircraft assumed to be equipped with the necessary systems as well as for a variation of the air-to-air communication range. In addition, we present average link durations, average amounts of hops to reach ground and numbers of connected aircraft for the different scenarios and identify general relations between the different factors and metrics. We will show, that communication range and equipage fraction significantly influence the connectivity of such networks.

Meta-Analysis of 49 Roche Oncology Trials Comparing Blinded Independent Central Review (BICR) and Local Evaluation to Assess the Value of BICR.

BACKGROUND: Blinded independent central review (BICR) of radiographic images is frequently conducted in oncology trials to address the potential bias of local evaluation (LE) of endpoints such as progression-free survival (PFS) and objective response rate (ORR). Given that BICR is a complex and costly process, we evaluated the agreement between LE- and BICR-based treatment effect results and the impact of BICR on regulatory decision-making. MATERIALS AND METHODS: Meta-analyses were performed using hazard ratios (HRs) for PFS and odds ratios (ORs) for ORR from all randomized Roche-supported oncology clinical trials during 2006-2020 that had both LE and BICR results (49 studies with a total of over 32 000 patients). RESULTS: Overall, the evaluation bias of LE overestimating the treatment effect compared with BICR based on PFS was numerically small and not clinically meaningful, especially for double-blind studies (HR ratio between BICR and LE: 1.044). A larger bias is more likely to occur in studies with open-label design, smaller sample sizes, or an unequal randomization ratio. The majority (87%) of the PFS comparisons led to the same statistical inference by BICR and LE. For ORR, a high degree of agreement between BICR and LE results was also observed (OR ratio of 1.065), although the agreement was slightly lower than for PFS. CONCLUSION: BICR did not notably impact the study interpretation nor drive the sponsor's regulatory submission decisions. Hence, if bias can be diminished by appropriate means, LE is deemed as reliable as BICR for certain study settings.

Calibration of in vitro multidrug resistance protein 1 substrate and inhibition assays as a basis to support the prediction of clinically relevant interactions in vivo.

The multidrug resistance protein 1 (MDR1) is known to limit brain penetration of drugs and play a key role in drug-drug interactions (DDIs). Theoretical cut-offs from regulatory guidelines are used to extrapolate MDR1 interactions from in vitro to in vivo. However, these cut-offs do not account for interlaboratory variability. Our aim was to calibrate our experimental system to allow better in vivo predictions. We selected 166 central nervous system (CNS) and non-CNS drugs to calibrate the MDR1 transport screening assay using Lewis lung cancer porcine kidney 1 epithelial cells overexpressing MDR1 (L-MDR1). A threshold efflux ratio (ER) of 2 was established as one parameter to assess brain penetration in lead optimization. The inhibitory potential of 57 molecules was evaluated using IC50 values based on the digoxin ER-IC50(ER)-or apparent permeability-IC50(Papp)-in L-MDR1 cells. Published clinical data for 68 DDIs involving digoxin as the victim drug were collected. DDI risk assessments were based on intestinal concentrations ([I2]) as well as unbound [I1u] and total plasma [I1T] concentrations. A receiver operating characteristic analysis identified an [I2]/IC50(ER) of 6.5 as the best predictor of a potential interaction with digoxin in patients. The model was further evaluated with a test set of 11 digoxin DDIs and 16 nondigoxin DDIs, resulting in only one false negative for each test set, no false positives among the digoxin DDIs, and two among the nondigoxin DDIs. Future refinements might include using cerebrospinal fluid to unbound plasma concentration ratios rather than therapeutic class, better estimation of [I2], and dynamic modeling of MDR1-mediated DDIs.

Translational PK/PD modeling for cardiovascular safety assessment of drug candidates: Methods and examples in drug development.

INTRODUCTION: Cardiovascular toxicity is a significant cause of candidate failure in drug development. Pharmacokinetic/pharmacodynamic (PK/PD) modeling may reduce attrition by improving the understanding of the relationship between drug exposure and changes in cardiovascular endpoints. Diverse examples are discussed that elucidate how modeling can facilitate the interpretation of cardiovascular safety data in animals and enable quantitative translation of preclinical findings to man. METHODS: Twelve compounds under development in diverse therapeutic areas were tested in cardiovascular safety studies in the telemetered beagle dog and cynomolgus monkey. Drug-induced changes observed in different cardiovascular endpoints (QRS complex and QTc interval of the ECG, heart rate, blood pressure, and myocardial contractility) were described by means of PK/PD modeling. A range of direct and indirect effect models were employed to characterize the plasma concentration-cardiovascular effect relationship for each compound. RESULTS: For every drug candidate the proposed PK/PD models appropriately described the cardiovascular effects observed in dog and monkey. Two of the compounds subsequently reached clinical development and cardiovascular data were generated in first-in-human clinical trials. For one drug candidate, a threshold model was used to describe QTc prolongation in the monkey and man. Blood pressure changes induced by the second compound were linked to plasma exposure in dog and human via an indirect response model. In both cases it was found that translational modeling accurately predicted the human response observed during clinical development. DISCUSSION: In this article, a range of PK/PD models are discussed that successfully described cardiovascular safety findings in the preclinical setting. Where clinical data were available, it was found that translational modeling enabled the accurate prediction of outcomes in man and facilitated the description of the therapeutic index. PK/PD modeling is thus demonstrated as a powerful tool to aid in the quantitative cardiovascular safety assessment of drug candidates and the optimization of early clinical study protocols.

PK/PD assessment in CNS drug discovery: Prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation.

The unbound drug concentration in brain parenchyma is considered to be the relevant driver for interaction with central nervous system (CNS) biological targets. Drug levels in cerebrospinal fluid (C_CSF) are frequently used surrogates for the unbound concentrations in brain. For drugs actively transported across the blood-brain barrier (BBB), C_CSF differs from unbound plasma concentration (Cu_p) to an extent that is commonly unknown. In this study, the relationship between CSF-to-unbound plasma drug partitioning in rats and the mouse Pgp (Mdr1a) efflux ratio (ER) obtained from in vitro transcellular studies has been investigated for a set of 61 CNS compounds exhibiting substantial diversity in chemical structure and physico-chemical properties. In order to understand the in vitro-in vivo extrapolation of Pgp efflux, a mechanistic model was derived relating in vivo CNS distribution kinetics to in vitro active transport. The model was applied to predict C_CSF from Cu_p and ER data for 19 proprietary Roche CNS drug candidates. The calculated CSF concentrations were correlated with CNS pharmacodynamic responses observed in rodent models. The correlation between in vitro and in vivo potency for different pharmacological endpoints indicated that the predicted C_CSF is a valuable surrogate of the concentration at the target site. Overall, C_CSF proved superior description of PK/PD data than unbound plasma or total brain concentration for Mdr1a substrates. Predicted C_CSF can be used as a default approach to understand the PK/PD relationships in CNS efficacy models and can support the extrapolation of efficacious brain exposure for new drug candidates from rodent to man.

Can we discover pharmacological promiscuity early in the drug discovery process?

The term 'pharmacological promiscuity' describes the activity of a single compound against multiple targets. When undesired, promiscuity is a major safety concern that needs to be detected as early as possible in the drug discovery process. The analysis of large datasets reveals that the majority of promiscuous compounds are characterized by recognizable molecular properties and structural motifs, the most important one being a basic center with a pK(a)(B)>6. These compounds interact with a small set of targets such as aminergic GPCRs; some of these targets attract surprisingly high hit rates. In this review, we discuss current trends in the assessment of pharmacological promiscuity and propose strategies to enable early detection and mitigation.

Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations.

The predictive power of four commonly used in silico tools for mutagenicity prediction (DEREK, Toxtree, MC4PC, and Leadscope MA) was evaluated in a comparative manner using a large, high-quality data set, comprising both public and proprietary data (F. Hoffmann-La Roche) from 9,681 compounds tested in the Ames assay. Satisfactory performance statistics were observed on public data (accuracy, 66.4-75.4%; sensitivity, 65.2-85.2%; specificity, 53.1-82.9%), whereas a significant deterioration of sensitivity was observed in the Roche data (accuracy, 73.1-85.5%; sensitivity, 17.4-43.4%; specificity, 77.5-93.9%). As a general tendency, expert systems showed higher sensitivity and lower specificity when compared to QSAR-based tools, which displayed the opposite behavior. Possible reasons for the performance differences between the public and Roche data, relating to the experimentally inactive to active compound ratio and the different coverage of chemical space, are thoroughly discussed. Examples of peculiar chemical classes enriched in false negative or false positive predictions are given, and the results of the combined use of the prediction systems are described.

Arabidopsis thaliana expresses a second functional flavonol synthase.

Arabidopsis thaliana L. produces flavonoid pigments, i.e. flavonols, anthocyanidins and proanthocyanidins, from dihydroflavonol substrates. A small family of putative flavonol synthase (FLS) genes had been recognized in Arabidopsis, and functional activity was attributed only to FLS1. Nevertheless, other FLS activities must be present, because A. thalianafls1 mutants still accumulate significant amounts of flavonols. The recombinant FLSs and leucoanthocyanidin dioxygenase (LDOX) proteins were therefore examined for their enzyme activities, which led to the identification of FLS3 as a second active FLS. This enzyme is therefore likely responsible for the formation of flavonols in the ldox/fls1-2 double mutant. These double mutant and biochemical data demonstrate for the first time that LDOX is capable of catalyzing the in planta formation of flavonols.

Development of benzophenone-based farnesyltransferase inhibitors as novel antimalarials.

The development of farnesyltransferase inhibitors directed against Plasmodium falciparum is a strategy towards new drugs against malaria. Previously, we described benzophenone-based farnesyltransferase inhibitors with high in vitro antimalarial activity but no in vivo activity. Through the introduction of a methylpiperazinyl moiety, farnesyltransferase inhibitors with in vivo antimalarial activity were obtained. Subsequently, a structure-based design approach was chosen to further improve the antimalarial activity of this type of inhibitor. As no crystal structure of the farnesyltransferase of the target organism is available, homology modeling was used to reveal differences between the active sites of the rat/human and the P. falciparum farnesyltransferase. Based on flexible docking data, the piperazinyl moiety was replaced by a N,N,N'-trimethylethylenediamine moiety. This resulted in an inhibitor with significantly improved in vitro and in vivo antimalarial activity. Furthermore, this inhibitor displayed a notable increase in selectivity towards malaria parasites relative to human cells.

Use of 3D QSAR models for database screening: a feasibility study.

The applicability and scope of 3D QSAR methods (CoMFA, CoMSIA) to screen databases are examined. A protocol requiring minimal user intervention has been established to align training and test set molecules using FlexS. As model system isozymes of human carbonic anhydrase (hCA) are used, all results are exemplified studying affinity toward hCA II and selectivity between hCA I and II. The predictive power of the obtained models is assessed through prediction of 663 compounds not included in the training set and compared to 2D QSAR models derived from fragment (MACCS) or property (VSA) based descriptors. The predictive power is evaluated with respect to the following criteria: the numerical, concerning the absolute accuracy of prediction, and the categorical, characterizing the ability to assign a compound to the correct activity class.

Use of biotin derivatives to probe conformational changes in proteins.

Sulfosuccinimidyl-6-(biotinamido) hexanoate and derivatives thereof covalently bind to the epsilon-amino group of lysine residues. Our observation that access of the biotin derivative to specific lysine residues depends on conformational properties of the entire polypeptide chain prompted us to investigate whether differential biotinylation patterns of a protein can be used as indicators for conformational changes. Bovine serum albumin is a soluble protein with characteristic unfolding kinetics upon exposure to high temperature. First, we show that biotinylation patterns of proteins are highly reproducible. Second, we demonstrate by mass spectrometry and tandem mass spectrometry that unfolding of the protein correlates with the accessibility of the biotin derivative to specific lysine residues. We have applied this experimental strategy to the analysis of a cell-surface protein, viz. the human band 3 anion exchanger of erythrocytes infected with the malaria parasite Plasmodium falciparum. We found that Lys(826) in a highly flexible loop can be biotinylated in non-infected (but not infected) erythrocytes, confirming earlier observations (Winograd, E., and Sherman, I. W. (2004) Mol. Biochem. Parasitol. 138, 83-87) based on epitope-specific monoclonal antibodies suggesting that this region undergoes a conformational change upon infection.

Integrated approach using protein and ligand information to analyze selectivity- and affinity-determining features of carbonic anhydrase isozymes.

The application and comparison of selected protein- and ligand-based approaches to elucidate factors important for affinity and selectivity towards the carbonic anhydrase isozymes I, II, and IV are described. Carbonic anhydrases are abundant in pro- and eukaryotes. These enzymes catalyze the reversible hydration of carbon dioxide to bicarbonate and H(+) ions and are thus involved in many important physiological and pathophysiological processes. Due to the fact that the human carbonic anhydrase family consists of 16 closely related isozymes, the design of selective inhibitors is a special challenge for medicinal chemists. In order to extract selectivity-determining features, we applied purely ligand-based 3D QSAR techniques as well as qualitative comparative molecular field analyses of the targets' binding sites using consensus principal component analysis (CPCA). The dataset for the QSAR studies was deliberately compiled from 1,748 inhibitors and comprises about 140 ligands, mainly of the sulfonamide type. Additionally, we employed the novel AFMoC approach, which intrinsically combines protein and ligand information. The simultaneous use of these different techniques gives deeper insight into selectivity and affinity-determining features and provides quantitative models for prediction.