Can immersive olfactory training serve as an alternative treatment for patients with smell dysfunction?

Objectives: Olfactory training (OT) has emerged as a first-line therapeutic approach to the management of olfactory dysfunction. Conventional OT (COT) involves the systematic home-based exposure to four distinct odors. Previous research has demonstrated that immersive OT (IOT) involving full-body exposure to dozens of distinct odors could also improve overall olfactory function. This study compared IOT and COT in terms of efficacy. Methods: A total of 60 patients were enrolled and assigned to three groups. The IOT group (n = 25) underwent immersive exposure to 64 odors once daily in a specialized theater. COT participants (n = 17) sniffed four typical odors in a set of four jars twice daily at home. A control group (n = 18) underwent passive observation. Olfactory function was assessed before and after training. Results: Significant improvements in composite threshold-discrimination-identification (TDI) scores were observed after training in both the IOT (mean difference = 2.5 ± 1.1. p = .030) and COT (mean difference = 4.2 ± 1.3, p = .002) groups. No changes were observed in the control group. A significantly higher proportion of patients in the COT group (41%) presented improvements of clinical importance (TDI ≥5.5) compared to the controls (p = .018). The improvements attained in the IOT group (20%) were less pronounced (p = .38). Conclusion: While IOT did not exhibit the same efficacy as COT in restoring olfactory function, it still demonstrated promising outcomes. Future efforts to advance olfactory recovery should focus on cross-modal integration. Level of Evidence: Level 3.

The state of the art in secondary pharmacology and its impact on the safety of new medicines.

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment.

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Predicting DNA-Reactivity of N-Nitrosamines: A Quantum Chemical Approach.

N-Nitrosamines (NAs) are a class of reactive organic chemicals that humans may be exposed to from environmental sources, food but also impurities in pharmaceutical preparations. Some NAs were identified as DNA-reactive mutagens and many of those have been classified as probable human carcinogens. Beyond high-potency mutagenic carcinogens that need to be strictly controlled, NAs of low potency need to be considered for risk assessment as well. NA impurities and nitrosylated products of active pharmaceutical ingredients (APIs) often arise from production processes or degradation. Most NAs require metabolic activation to ultimately become carcinogens, and their activation can be appropriately described by first-principles computational chemistry approaches. To this end, we treat NA-induced DNA alkylation as a series of subsequent association and dissociation reaction steps that can be calculated stringently by density functional theory (DFT), including α-hydroxylation, proton transfer, hydroxyl elimination, direct SN2/SNAr DNA alkylation, competing hydrolysis and SN1 reactions. Both toxification and detoxification reactions are considered. The activation reactions are modeled by DFT at a high level of theory with an appropriate solvent model to compute Gibbs free energies of the reactions (thermodynamical effects) and activation barriers (kinetic effects). We study congeneric series of aliphatic and cyclic NAs to identify trends. Overall, this work reveals detailed insight into mechanisms of activation for NAs, suggesting that individual steric and electronic factors have directing and rate-determining influence on the formation of carbenium ions as the ultimate pro-mutagens and thus carcinogens. Therefore, an individual risk assessment of NAs is suggested, as exemplified for the complex API-like 4-(N-nitroso-N-methyl)aminoantipyrine which is considered as low-potency NA by in silico prediction.

Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac purkinje cells for proarrhythmia risk prediction.

The preclinical identification of drug-induced cardiotoxicity and its translation into human risk are still major challenges in pharmaceutical drug discovery. The ICH S7B Guideline and Q&A on Clinical and Nonclinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential promotes human in silico drug trials as a novel tool for proarrhythmia risk assessment. To facilitate the use of in silico data in regulatory submissions, explanatory control compounds should be tested and documented to demonstrate consistency between predictions and the historic validation data. This study aims to quantify drug-induced electrophysiological effects on in silico cardiac human Purkinje cells, to compare them with existing in vitro rabbit data, and to assess their accuracy for clinical pro-arrhythmic risk predictions. The effects of 14 reference compounds were quantified in simulations with a population of in silico human cardiac Purkinje models. For each drug dose, five electrophysiological biomarkers were quantified at three pacing frequencies, and results compared with available in vitro experiments and clinical proarrhythmia reports. Three key results were obtained: 1) In silico, repolarization abnormalities in human Purkinje simulations predicted drug-induced arrhythmia for all risky compounds, showing higher predicted accuracy than rabbit experiments; 2) Drug-induced electrophysiological changes observed in human-based simulations showed a high degree of consistency with in vitro rabbit recordings at all pacing frequencies, and depolarization velocity and action potential duration were the most consistent biomarkers; 3) discrepancies observed for dofetilide, sotalol and terfenadine are mainly caused by species differences between humans and rabbit. Taken together, this study demonstrates higher accuracy of in silico methods compared to in vitro animal models for pro-arrhythmic risk prediction, as well as a high degree of consistency with in vitro experiments commonly used in safety pharmacology, supporting the potential for industrial and regulatory adoption of in silico trials for proarrhythmia prediction.

Accurate in silico simulation of the rabbit Purkinje fiber electrophysiological assay to facilitate early pharmaceutical cardiosafety assessment: Dream or reality?

As a branch of quantitative systems toxicology, in silico simulations are of growing attractiveness to guide preclinical cardiosafety risk assessments. Traditionally, a cascade of in vitro/in vivo assays has been applied in pharmaceutical research to screen out molecules at risk for cardiac side effects and prevent subsequent risk for patients. Drug cardiosafety assessments typically employ early mechanistic, hazard-oriented in silico/in vitro assays for compound inhibition of cardiac ion channels, followed by induced pluripotent stem cells (iPSCs) or tissue-based models such as the rabbit Purkinje fiber assay, which includes the major mechanisms contributing to action potential (AP) genesis. Additionally, multiscale simulation techniques based on mathematical models have become available, which are performed in silico 'at the heart' of compound triage to substitute Purkinje tests and increase translatability through mechanistic interpretability. To adhere to the 3R principle and reduce animal experiments, we performed a comparative benchmark and investigated a variety of mathematical cardiac AP models, including a newly developed minimalistic model specifically tailored to the AP of rabbit Purkinje cells, for their ability to substitute experiments. The simulated changes in AP duration (dAPD90) at increasing drug concentrations were compared to experimental results from 588 internal Purkinje fiber studies covering 555 different drugs with diverse modes of action. Using our minimalistic model, 80% of the Purkinje experiments could be quantitatively reproduced. This result allows for significant saving of experimental effort in early research and justifies the embedding of electrophysiological simulations into the DMTA (Design, Make, Test, Analyze) cycle in pharmaceutical compound optimization.

Rational Design of Highly Potent, Selective, and Bioavailable SGK1 Protein Kinase Inhibitors for the Treatment of Osteoarthritis.

The serine/threonine kinase SGK1 is an activator of the ß-catenin pathway and a powerful stimulator of cartilage degradation that is found to be upregulated under genomic control in diseased osteoarthritic cartilage. Today, no oral disease-modifying treatments are available and chronic treatment in this indication sets high requirements for the drug selectivity, pharmacokinetic, and safety profile. We describe the identification of a highly selective druglike 1H-pyrazolo[3,4-d]pyrimidine SGK1 inhibitor 17a that matches both safety and pharmacokinetic requirements for oral dosing. Rational compound design was facilitated by a novel hSGK1 co-crystal structure, and multiple ligand-based computer models were applied to guide the chemical optimization of the compound ADMET and selectivity profiles. Compounds were selected for subchronic proof of mechanism studies in the mouse femoral head cartilage explant model, and compound 17a emerged as a druglike SGK1 inhibitor, with a highly optimized profile suitable for oral dosing as a novel, potentially disease-modifying agent for osteoarthritis.

Application of Deep Neural Network Models in Drug Discovery Programs.

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery.

A practical guide to secondary pharmacology in drug discovery.

Secondary pharmacological profiling is increasingly applied in pharmaceutical drug discovery to address unwanted pharmacological side effects of drug candidates before entering the clinic. Regulators, drug makers and patients share a demand for deep characterization of secondary pharmacology effects of novel drugs and their metabolites. The scope of such profiling has therefore expanded substantially in the past two decades, leading to the implementation of broad in silico profiling methods and focused in vitro off-target screening panels, to identify liabilities, but also opportunities, as early as possible. The pharmaceutical industry applies such panels at all stages of drug discovery routinely up to early development. Nevertheless, target composition, screening technologies, assay formats, interpretation and scheduling of panels can vary significantly between companies in the absence of dedicated guidelines. To contribute towards best practices in secondary pharmacology profiling, this review aims to summarize the state-of-the art in this field. Considerations are discussed with respect to panel design, screening strategy, implementation and interpretation of the data, including regulatory perspectives. The cascaded, or integrated, use of in silico and off-target profiling allows to exploit synergies for comprehensive safety assessment of drug candidates.

Computational Investigation of Drug Phototoxicity: Photosafety Assessment, Photo-Toxophore Identification, and Machine Learning.

One of the most appreciated capabilities of computational toxicology is to support the design of pharmaceuticals with reduced toxicological hazard. To this end, we have strengthened our drug photosafety assessments by applying novel computer models for the anticipation of in vitro phototoxicity and human photosensitization. These models are typically used in pharmaceutical discovery projects as part of the compound toxicity assessments and compound optimization methods. To ensure good data quality and aiming at models with global applicability we separately compiled and curated highly chemically diverse data sets from 3T3 NRU phototoxicity reports (450 compounds) and clinical photosensitization alerts (1419 compounds) which are provided as supplements. The latter data gives rise to a comprehensive list of explanatory fragments for visual guidance, termed phototoxophores, by application of a Bayesian statistics approach. To extend beyond the domain of well sampled fragments we applied machine learning techniques based on explanatory descriptors such as pharmacophoric fingerprints or, more important, accurate electronic energy descriptors. Electronic descriptors were extracted from quantum chemical computations at the density functional theory (DFT) level. Accurate UV/vis spectral absorption descriptors and pharmacophoric fingerprints turned out to be necessary for predictive computer models, which were both derived from Deep Neural Networks but also the simpler Random Decision Forests approach. Model accuracies of 83-85% could typically be reached for diverse test data sets and other company in-house data, while model sensitivity (the capability of correctly detecting toxicants) was even better, reaching 86%-90%. Importantly, a computer model-triggered response-map allowed for graphical/chemical interpretability also in the case of previously unknown phototoxophores. The photosafety models described here are currently applied in a prospective manner for the hazard identification, prioritization, and optimization of newly designed molecules.

Mechanistic investigations of the liver toxicity of the free fatty acid receptor 1 agonist fasiglifam (TAK875) and its primary metabolites.

For fasiglifam (TAK875) and its metabolites the substance-specific mechanisms of liver toxicity were studied. Metabolism studies were run to identify a putatively reactive acyl glucuronide metabolite. In vitro cytotoxicity and caspase 3/7 activation were assessed in primary human and dog hepatocytes in 2D and 3D cell culture. Involvement of glutathione (GSH) detoxication system in mediating cytotoxicity was determined by assessing potentiation of cytotoxicity in a GSH depleted in vitro system. In addition, potential mitochondrial liabilities of the compounds were assessed in a whole-cell mitochondrial functional assay. Fasiglifam showed moderate cytotoxicity in human primary hepatocytes in the classical 2D cytotoxicity assays and also in the complex 3D human liver microtissue (hLiMT) after short-term treatment (24 hours or 48 hours) with TC50 values of 56 to 68 µM (adenosine triphosphate endpoint). The long-term treatment for 14 days in the hLiMT resulted in a slight TC50 shift over time of 2.7/3.6 fold lower vs 24-hour treatment indicating possibly a higher risk for cytotoxicity during long-term treatment. Cellular GSH depletion and impairment of mitochondrial function by TAK875 and its metabolites evaluated by Seahorse assay could not be found being involved in DILI reported for TAK875. The acyl glucuronide metabolites of TAK875 have been finally identified to be the dominant reason for liver toxicity.

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.

Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from public or corporate databases. Both the choice of DNN hyperparameters and the type and quantity of molecular descriptors were found to be important for successful DNN modeling. Alternate learning of multiple ADME-Tox properties, resulting in a multitask approach, performs statistically superior on most studied data sets in comparison to DNN single-task models and also provides a scalable method to predict ADME-Tox properties from heterogeneous data. For example, predictive quality using external validation sets was improved from R2 of 0.6 to 0.7 comparing single-task and multitask DNN networks from human metabolic lability data. Besides statistical evaluation, a new visualization approach is introduced to interpret DNN models termed "response map", which is useful to detect local property gradients based on structure fragmentation and derivatization. This method is successfully applied to visualize fragmental contributions to guide further design in drug discovery programs, as illustrated by CRCX3 antagonists and renin inhibitors, respectively.

In vitro secondary pharmacological profiling: An IQ-DruSafe industry survey on current practices.

INTRODUCTION: In 2015, IQ DruSafe conducted a survey of its membership to identify industry practices related to in vitro off target pharmacological profiling of small molecules. METHODS: An anonymous survey of 20 questions was submitted to IQ-DruSafe representatives. Questions were designed to explore screening strategies, methods employed and experience of regulatory interactions related to in vitro secondary pharmacology profiling. RESULTS: The pharmaceutical industry routinely utilizes panels of in vitro assays to detect undesirable off-target interactions of new chemical entities that are deployed at all stages of drug discovery and early development. The formats, approaches and size of panels vary between companies, in particular i) choice of assay technology; ii) test concentration (single vs. multiple concentrations) iii) rationale for targets and panels selection (taking into account organizational experience, primary target, therapeutic area, availability at service providers) iv) threshold level for significant interaction with a target and v) data interpretation. Data are generated during the early phases of drug discovery, principally before in vivo GLP studies (i.e., hit-to-lead, lead optimization, development candidate selection) and used to contextualize in vivo non-clinical and clinical findings. Data were included in regulatory documents, and around half of respondents experienced regulatory questions about the significance of the results. CONCLUSION: While it seems that in vitro secondary pharmacological profiling is generally considered valuable across the industry, particularly as a tool in early phases of drug discovery for small molecules, there is only loose consensus on testing paradigm, the required interpretation and suitable follow up strategies to fully understand potential risk.

Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope.

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

Synthesis and multiparametric evaluation of thiadiazoles and oxadiazoles as diacylglycerol acyltransferase type 1 inhibitors.

Chemical modulation of a formerly disclosed DGAT-1 inhibitor resulted in the identification of a compound with a suitable profile for preclinical development. Optimisation of solubility is discussed and a PK/PD study is presented.

Discovery of N-[4-(1H-Pyrazolo[3,4-b]pyrazin-6-yl)-phenyl]-sulfonamides as Highly Active and Selective SGK1 Inhibitors.

From a virtual screening starting point, inhibitors of the serum and glucocorticoid regulated kinase 1 were developed through a combination of classical medicinal chemistry and library approaches. This resulted in highly active small molecules with nanomolar activity and a good overall in vitro and ADME profile. Furthermore, the compounds exhibited unusually high kinase and off-target selectivity due to their rigid structure.

Predictive in silico off-target profiling in drug discovery.

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein-ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

3D-QSAR based on quantum-chemical molecular fields: toward an improved description of halogen interactions.

Current 3D-QSAR methods such as CoMFA or CoMSIA make use of classical force-field approaches for calculating molecular fields. Thus, they can not adequately account for noncovalent interactions involving halogen atoms like halogen bonds or halogen-π interactions. These deficiencies in the underlying force fields result from the lack of treatment of the anisotropy of the electron density distribution of those atoms, known as the "σ-hole", although recent developments have begun to take specific interactions such as halogen bonding into account. We have now replaced classical force field derived molecular fields by local properties such as the local ionization energy, local electron affinity, or local polarizability, calculated using quantum-mechanical (QM) techniques that do not suffer from the above limitation for 3D-QSAR. We first investigate the characteristics of QM-based local property fields to show that they are suitable for statistical analyses after suitable pretreatment. We then analyze these property fields with partial least-squares (PLS) regression to predict biological affinities of two data sets comprising factor Xa and GABA-A/benzodiazepine receptor ligands. While the resulting models perform equally well or even slightly better in terms of consistency and predictivity than the classical CoMFA fields, the most important aspect of these augmented field-types is that the chemical interpretation of resulting QM-based property field models reveals unique SAR trends driven by electrostatic and polarizability effects, which cannot be extracted directly from CoMFA electrostatic maps. Within the factor Xa set, the interaction of chlorine and bromine atoms with a tyrosine side chain in the protease S1 pocket are correctly predicted. Within the GABA-A/benzodiazepine ligand data set, PLS models of high predictivity resulted for our QM-based property fields, providing novel insights into key features of the SAR for two receptor subtypes and cross-receptor selectivity of the ligands. The detailed interpretation of regression models derived using improved QM-derived property fields thus provides a significant advantage by revealing chemically meaningful correlations with biological activity and helps in understanding novel structure-activity relationship features. This will allow such knowledge to be used to design novel molecules on the basis of interactions additional to steric and hydrogen-bonding features.

Thiadiazoles as new inhibitors of diacylglycerol acyltransferase type 1.

A novel class of DGAT1 inhibitors containing a thiadiazole core has been discovered. Chemical optimization lead to inhibitors of human DGAT1 with an appropriate ADME profile and that show in vivo activity in target tissues.