Mutagenicity is one of the numerous adverse properties of a compound that hampers its potential to become a marketable drug. Toxic properties can often be related to chemical structure, more specifically, to particular substructures, which are generally identified as toxicophores. A number of toxicophores have already been identified in the literature. This study aims at increasing the current degree of reliability and accuracy of mutagenicity predictions by identifying novel toxicophores from the application of new criteria for toxicophore rule derivation and validation to a considerably sized mutagenicity dataset. For this purpose, a dataset of 4337 molecular structures with corresponding Ames test data (2401 mutagens and 1936 nonmutagens) was constructed. An initial substructure-search of this dataset showed that most mutagens were detected by applying only eight general toxicophores. From these eight, more specific toxicophores were derived and approved by employing chemical and mechanistic knowledge in combination with statistical criteria. A final set of 29 toxicophores containing new substructures was assembled that could classify the mutagenicity of the investigated dataset with a total classification error of 18%. Furthermore, mutagenicity predictions of an independent validation set of 535 compounds were performed with an error percentage of 15%. Since these error percentages approach the average interlaboratory reproducibility error of Ames tests, which is 15%, it was concluded that these toxicophores can be applied to risk assessment processes and can guide the design of chemical libraries for hit and lead optimization.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established.In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper.To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by Accepted ArticleThis article is protected by copyright. All rights reserved regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

A novel 3D QSAR approach, comparative spectra analysis (CoSA), in which molecular spectra are used as three-dimensional molecular descriptors for the prediction of biological activities, is presented and discussed. To this purpose, experimentally determined 1H NMR, mass, and IR spectra, as well as simulated IR and 13C NMR spectra, for a set of 45 diverse progestagens are converted by a program, SpecMat, into matrixes, which are subsequently employed in a multivariate regression analysis (PLS). The results are compared with those resulting from a comparative molecular field analysis (CoMFA). When used individually, spectral descriptors yield better correlations and predictions than molecular field descriptors. A combination of spectral descriptors with other descriptors, either spectral or molecular field in nature, leads in most cases to models that are statistically superior to the ones obtained by their corresponding individual spectral or molecular field descriptors.

Org 26576 acts by modulating ionotropic AMPA-type glutamate receptors to enhance glutamatergic neurotransmission. The aim of this Phase 1b study (N=54) was to explore safety, tolerability, pharmacokinetics, and pharmacodynamics of Org 26576 in depressed patients. Part I (N=24) evaluated the maximum tolerated dose (MTD) and optimal titration schedule in a multiple rising dose paradigm (range 100 mg BID to 600 mg BID); Part II (N=30) utilized a parallel groups design (100 mg BID, 400 mg BID, placebo) to examine all endpoints over a 28-day dosing period. Based on the number of moderate intensity adverse events reported at the 600 mg BID dose level, the MTD established in Part I was 450 mg BID. Symptomatic improvement as measured by the Montgomery-Asberg Depression Rating Scale was numerically greater in the Org 26576 groups than in the placebo group in both study parts. In Part II, the 400 mg BID dose was associated with improvements in executive functioning and speed of processing cognitive tests. Org 26576 was also associated with growth hormone increases and cortisol decreases at the end of treatment but did not influence prolactin or brain-derived neurotrophic factor. The quantitative electroencephalogram index Antidepressant Treatment Response at Week 1 was able to significantly predict symptomatic response at endpoint in the active treatment group, as was early improvement in social acuity. Overall, Org 26576 demonstrated good tolerability and pharmacokinetic properties in depressed patients, and pharmacodynamic endpoints suggested that it may show promise in future well-controlled, adequately powered proof of concept trials.

The S1 ↔ S0 transitions of the “proton sponge” 1,8-bis(dimethylamino)naphthalene have been studied by experiment and ab initio calculations. Fluorescence excitation and single vibronic level emission spectroscopy on the sample seeded in a supersonic expansion lead to the conclusion that the molecule can adopt two conformations in the ground state. This conclusion is supported by ab initio calculations at the HF/6-31G* level. The most stable conformer is shown to carry the spectroscopic characteristics of the naphthalene chromophore, while torsional motions of the dimethylamino groups dominate the spectroscopy of the other conformer.

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The added value of in silico models (including quantitative systems pharmacology models) for drug development is now unanimously recognized. It is, therefore, important that the standards used are commonly acknowledged by all the parties involved. On April 25 and 26, 2019, a multistakeholder workshop on the validation challenges for in silico models in drug development was organized in Belgium. As an outcome, a White Paper is foreseen in 2020 on standards for in silico model verification and validation. CURRENT STATUS, GAPS, AND CHALLENGES IN ASSESSMENT OF MODELS FOR REGULATORY SUBMISSIONSDrug research, design, and development has a long-standing tradition in the use of in silico methodologies. In the context of clinical drug development Quantitative Structure-Property Relationship models in general and Quantitative Structure-Activity Relationship (QSAR) methods in particular, as well as pharmacometric approaches like population pharmacokinetics, pharmacokinetics (PKs)/pharmacodynamics, exposure-response, and physiology-based pharmacokinetics (PBPK) models are well-known. However, the in silico toolbox is rapidly expanding beyond these traditional/historical modeling technologies and new ones have emerged the last decades, including multiphysics simulations, the so-called systems medicine/pharmacology models (QSP) and clinical trial simulation tools (in silico clinical trials). In the remainder of this document, the term in silico models will be used to describe the collection of all the aforementioned modeling technologies.The added value of in silico models for drug development is now unanimously recognized by the scientific community. 1,2 Irrespective of the model used and the concerned part of the drug development pipeline, the evidence generated from these models, also called digital evidence, might eventually be included in regulatory submissions. In that case, the incorporation of digital evidence needs to follow standards of data/evidence generation, analysis, and reporting to enable the regulatory bodies to efficiently perform an adequate assessment of the submitted material.It is, therefore, of utmost importance that the standards to be considered are commonly acknowledged by all the involved parties (regulators, health technology assessment (HTA) agencies, academia, industry, regulators, and patients) and are relevant for all the types of models that can be included in regulatory submissions. The endorsement of these standards by regulators is particularly valuable because regulators generally provide guidance for data generation and reporting back to sponsors (industry or academia) thereby accelerating the uptake of the standards in the entire community and in the healthcare systems.Guidance documents have been published for QSAR models, 3 population PK models, 4 PK/pharmacodynamic or exposure-response models, 5,6 and more recently PBPK models, both by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). 7,8 However, these guidelines are not fully applicable to all emergi...