New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets.

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.This work was supported as part of the PhD project of K.A.G funded by the European Research Council and AstraZeneca Early Oncology TD

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Time-dependent density functional theory/discrete reaction field spectra of open shell systems:The visual spectrum of [Fe-III(PyPepS)(2)](-) in aqueous solution

We report the calculated visible spectrum of [Fe-III(PyPepS)(2)](-) in aqueous solution. From all-classical molecular dynamics simulations on the solute and 200 water molecules with a polarizable force field, 25 solute/solvent configurations were chosen at random from a 50 ps production run and subjected the systems to calculations using time-dependent density functional theory (TD-DFT) for the solute, combined with a solvation model in which the water molecules carry charges and polarizabilities. In each calculation the first 60 excited states were collected in order to span the experimental spectrum. Since the solute has a doublet ground state several excitations to states are of type "three electrons in three orbitals," each of which gives rise to a manifold of a quartet and two doublet states which cannot properly be represented by single Slater determinants. We applied a tentative scheme to analyze this type of spin contamination in terms of Delta and Delta transitions between the same orbital pairs. Assuming the associated states as pure single determinants obtained from restricted calculations, we construct conformation state functions (CFSs), i.e., eigenfunctions of the Hamiltonian (S) over cap (z) and (S) over cap (2), for the two doublets and the quartet for each Delta,Delta pair, the necessary parameters coming from regular and spin-flip calculations. It appears that the lower final states remain where they were originally calculated, while the higher states move up by some tenths of an eV. In this case filtering out these higher states gives a spectrum that compares very well with experiment, but nevertheless we suggest investigating a possible (re)formulation of TD-DFT in terms of CFSs rather than determinants

In silico ADME in drug design – enhancing the impact

Each year the pharmaceutical industry makes thousands of compounds, many of which do not meet the desired efficacy or pharmacokinetic properties, describing the absorption, distribution, metabolism and excretion (ADME) behavior. Parameters such as lipophilicity, solubility and metabolic stability can be measured in high throughput in vitro assays. However, a compound needs to be synthesized in order to be tested. In silico models for these endpoints exist, although with varying quality. Such models can be used before synthesis and, together with a potency estimation, influence the decision to make a compound. In practice, it appears that often only one or two predicted properties are considered prior to synthesis, usually including a prediction of lipophilicity. While it is important to use all information when deciding which compound to make, it is somewhat challenging to combine multiple predictions unambiguously. This work investigates the possibility of combining in silico ADME predictions to define the minimum required potency for a specified human dose with sufficient confidence. Using a set of drug discovery compounds, in silico predictions were utilized to compare the relative ranking based on minimum potency calculation with the outcomes from the selection of lead compounds. The approach was also tested on a set of marketed drugs and the influence of the input parameters investigated

PHEE : a dataset for pharmacovigilance event extraction from text

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area

PHEE : a dataset for pharmacovigilance event extraction from text

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area

Modeling the Mechanism of Action of a DGAT1 Inhibitor Using a Causal Reasoning Platform

Triglyceride accumulation is associated with obesity and type 2 diabetes. Genetic disruption of diacylglycerol acyltransferase 1 (DGAT1), which catalyzes the final reaction of triglyceride synthesis, confers dramatic resistance to high-fat diet induced obesity. Hence, DGAT1 is considered a potential therapeutic target for treating obesity and related metabolic disorders. However, the molecular events shaping the mechanism of action of DGAT1 pharmacological inhibition have not been fully explored yet. Here, we investigate the metabolic molecular mechanisms induced in response to pharmacological inhibition of DGAT1 using a recently developed computational systems biology approach, the Causal Reasoning Engine (CRE). The CRE algorithm utilizes microarray transcriptomic data and causal statements derived from the biomedical literature to infer upstream molecular events driving these transcriptional changes. The inferred upstream events (also called hypotheses) are aggregated into biological models using a set of analytical tools that allow for evaluation and integration of the hypotheses in context of their supporting evidence. In comparison to gene ontology enrichment analysis which pointed to high-level changes in metabolic processes, the CRE results provide detailed molecular hypotheses to explain the measured transcriptional changes. CRE analysis of gene expression changes in high fat habituated rats treated with a potent and selective DGAT1 inhibitor demonstrate that the majority of transcriptomic changes support a metabolic network indicative of reversal of high fat diet effects that includes a number of molecular hypotheses such as PPARG, HNF4A and SREBPs. Finally, the CRE-generated molecular hypotheses from DGAT1 inhibitor treated rats were found to capture the major molecular characteristics of DGAT1 deficient mice, supporting a phenotype of decreased lipid and increased insulin sensitivity

Information systems for collaborating versus transacting: Impact on manufacturing plant performance in the presence of demand volatility⋆

Research at the nexus of operations management and information systems suggests that manufacturing plants may benefit from the utilization of information systems for collaborating and transacting with suppliers and customers. The objective of this study is to examine the extent to which value generated by information systems for collaborating versus transacting is contingent upon demand volatility. We analyze a unique dataset assembled from non‐public U.S. Census Bureau data of manufacturing plants. Our findings suggest that when faced with volatile demand, plants employing information systems for collaborating with suppliers and customers experience positive and significant benefits to performance, in terms of both labor productivity and inventory turnover. In contrast, results suggest that plants employing information systems for transacting in volatile environments do not experience such benefits. Further exploratory analysis suggests that in the context of demand volatility, these two distinct dimensions of IT‐based integration have differing performance implications at different stages of the production process in terms of raw‐materials inventory and finished‐goods inventory, but not in terms of work‐in‐process inventory. Taken together, our study contributes to theoretical and managerial understanding of the contingent value of information systems in volatile demand conditions in the supply chain context.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147128/1/joom313.pd