Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Synchronous down-modulation of miR-17 family members is an early causative event in the retinal angiogenic switch

Six members of the microRNA-17 (miR-17) family were mapped to three different chromosomes, although they share the same seed sequence and are predicted to target common genes, among which are those encoding hypoxia-inducible factor-1α (HIF1A) and VEGFA. Here, we evaluated the in vivo expression profile of the miR-17 family in the murine retinopathy of prematurity (ROP) model, whereby Vegfa expression is highly enhanced at the early stage of retinal neovascularization, and we found simultaneous reduction of all miR-17 family members at this stage. Using gene reporter assays, we observed binding of these miRs to specific sites in the 3′ UTRs of Hif1a and Vegfa. Furthermore, overexpression of these miRs decreased HIF1A and VEGFA expression in vitro. Our data indicate that this miR-17 family elicits a regulatory synergistic down-regulation of Hif1a and Vegfa expression in this biological model. We propose the existence of a coordinated regulatory network, in which diverse miRs are synchronously regulated to target the Hif1a transcription factor, which in turn, potentiates and reinforces the regulatory effects of the miRs on Vegfa to trigger and sustain a significant physiological response

Investigative safety strategies to improve success in drug development

Understanding and reducing attrition rate remains a key challenge in drug development. Preclinical and clinical safety issues still represent about 40% of drug discontinuation, of which cardiac and liver toxicities are the leading reasons. Reducing attrition rate can be achieved by various means, starting with a comprehensive evaluation of the potential safety issues associated to the primary target followed by an evaluation of undesirable secondary targets. To address these risks, a risk mitigation plan should be built at very early development stages, using a panel of in silico, in vitro, and in vivo models. While most pharmaceutical companies have developed robust safety strategies to de-risk genotoxicity and cardiotoxicity issues, partly driven by regulatory requirements; safety issues affecting other organs or systems, such as the central nervous system, liver, kidney, or gastro-intestinal system are less commonly addressed during early drug development. This paper proposes some de-risking strategies that can be applied to these target organ systems, including the use of novel biomarkers that can be easily integrated in both preclinical and clinical studies. Experiments to understand the mechanisms’ underlying toxicity are also important. Two examples are provided to demonstrate how such mechanistic studies can impact drug development. Novel trends in investigative safety are reviewed, such as computational modeling, mitochondrial toxicity assessment, and imaging technologies. Ultimately, understanding the predictive value of non-clinical safety testing and its translatability to humans will enable to optimize assays in order to address the key objectives of the drug discovery process, i.e., hazard identification, risk assessment, and mitigation

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information

Structural Aspects of Halocuprate and Haloargentate Anions in Supramolecular Systems

There is a growing interest in designing and engineering crystals of compounds with desired structures and properties. In this context, a better understanding of the intermolecular interactions present in crystals is of importance and much work is currently being directed towards this goal. The work in this thesis is centered on understanding the structure-determining forces in compounds containing halocuprate and haloargentate anions. Several new complexes have been synthesised and their structures determined using X-ray diffraction. Subsequently, the crystal packings have been analysed with respect to attractive intermolecular interactions. In some cases, computational calculations using Force Field methods or Density Functional methods were employed to better understand the energetics involved. Experimental work also included solution investigations using Mass Spectrometry. The results show that the systems studied are generally structurally flexible and the specific anionic species are often a consequence of attractive cation-cation and cation-halide interactions. These interactions are believed to be important both in the crystal and in solutions of the relevant species. Two new halocuprate anions of unusual geometry have been isolated: [Cu(II)X3]- (1 & 2) and [Cu(II)X3\ub7MeCN]- (3), where X = Cl/Br. The geometry of 1 and 2 is distorted trigonal planar and in 3 the coordination number of copper is 3+1 and the anion has a distorted trigonal pyramidal geometry with the acetonitrile molecule situated at the apex of the pyramid

Halocuprates(I) crystallising with the Ph3PNPPh3 + cation: preparation and structural characterisation of (Ph3PNPPh3)2[Cu4Br6] and (Ph3PNPPh3)[CuBrCl]

Two halocuprates(I) crystallising with the Ph3PNPPh3+ cation, (Ph3PNPPh3)(2)[Cu4Br6] (1) and (Ph3PNPPh3)[CuBrCl] (2), have been prepared and characterised by means of crystal-structure determination. The anion in 1 is a tetranuclear species composed of vertex-sharing copper(I)-bromide triangles. It may, alternatively, be described in terms of an octahedron of bromide ions containing a tetrahedron of copper(l) ions, There are two possible orientations of the copper(l) tetrahedron which results in partial occupancy of the crystallographic copper(l) sites. The anion in 2 is also disordered with bromide and chloride occupying the halide sites to differing degrees, resulting in the composition [CuBr1.46Cl0.54](-). In both compounds the Ph3PNPPh3+ cation exhibits an intramolecular claw-like conformation with offset face-to-face orientation of two phenyl rings but the orientation of the cation claws with respect to the anion differs considerably between the two compounds

Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose–Response Inference on hERG Inhibition Models

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose–response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC50 for hERG inhibition is estimated from diverse historical proprietary data. The IC50 derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC50s derived from six-point dose–response curves. Similar IC50 estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC50 data were used to develop a robust quantitative model. The model’s MAE (0.47) and R2 (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints