The continuing evolution of the drug discovery process in the pharmaceutical industry

Generation of new inhibitors of selected cytochrome P450 subtypes- In silico study

Physicochemical and pharmacokinetic compound profile has crucial impact on compound potency to become a future drug. Ligands with desired activity profile cannot be used for treatment if they are characterized by unfavourable physicochemical or ADMET properties. In the study, we consider metabolic stability and focus on selected subtypes of cytochrome P450 - proteins, which take part in the first phase of compound transformations in the organism. We develop a protocol for generation of new potential inhibitors of selected cytochrome isoforms. Its subsequent stages are composed of generation and assessment of new derivatives of known cytochrome inhibitors, docking and evaluation of the compound possible inhibition on the basis of the obtained ligand-protein complexes. Besides the library of new potential agents inhibiting particular cytochrome subtypes, we also prepare a graph neural network that predicts the change in activity for all modifications of the starting molecule. In addition, we perform a systematic statistical study on the influence of particular substitutions on the potential inhibition properties of generated compounds (both mono- and di-substitutions are considered), provide explanations of the inhibitory predictions and prepare an on-line visualization platform enabling manual inspection of the results. The developed methodology can greatly support the design of new cytochrome P450 inhibitors with the overarching goal of generation of new metabolically stable compounds. It enables instant evaluation of possible compound-cytochrome interactions and selection of ligands with the highest potential of possessing desired biological activity.

How can SHAP values help to shape metabolic stability of chemical compounds?

Background

Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in the evaluation and optimization of their physicochemical and pharmacokinetic properties. Such features are not less important in terms of the possible turn of a compound into a future drug than its desired affinity profile towards considered proteins. In the study, we focus on metabolic stability, which determines the time that the compound can act in the organism and play its role as a drug. Due to great complexity of xenobiotic transformation pathways in the living organisms, evaluation and optimization of metabolic stability remains a big challenge.

Results

Here, we present a novel methodology for the evaluation and analysis of structural features influencing metabolic stability. To this end, we use a well-established explainability method called SHAP. We built several predictive models and analyse their predictions with the SHAP values to reveal how particular compound substructures influence the model’s prediction. The method can be widely applied by users thanks to the web service, which accompanies the article. It allows a detailed analysis of SHAP values obtained for compounds from the ChEMBL database, as well as their determination and analysis for any compound submitted by a user. Moreover, the service enables manual analysis of the possible structural modifications via the provision of analogous analysis for the most similar compound from the ChEMBL dataset.

Conclusions

To our knowledge, this is the first attempt to employ SHAP to reveal which substructural features are utilized by machine learning models when evaluating compound metabolic stability. The accompanying web service for metabolic stability evaluation can be of great help for medicinal chemists. Its significant usefulness is related not only to the possibility of assessing compound stability, but also to the provision of information about substructures influencing this parameter. It can assist in the design of new ligands with improved metabolic stability, helping in the detection of privileged and unfavourable chemical moieties during stability optimization. The tool is available at https://metstab-shap.matinf.uj.edu.pl/.

Mol-CycleGAN: a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improve the compound design process, we introduce Mol-CycleGAN-a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

Wine Compounds as a Source for HTS Screening Collections. A Feasibility Study

High throughput screening (HTS) is extensively used to identify hit and lead compounds in drug discovery programmes. Designing quality screening libraries is a challenge in terms of water solubility, stability and potential oral bioavailability of the compounds. Wines are widely consumed and wine compounds are inherently water soluble, stable and relatively non-toxic. Furthermore, many wine compounds have been proved health-beneficial. To evaluate the feasibility to use wine compounds 3317 were collected from the literature. Their physiochemical properties were evaluated with main stream molecular descriptors. According to the results ∼25 % of the compounds are lead-like; nearly 80 % lie within drug-like chemical space and finally 90 % conform to known drug space (KDS). The rotatable bonds descriptor was the most effective defining lead-like space. The results suggest that many of the wine compounds are interesting and suitable candidates for screening libraries after suitable filtering.

Improving the quality of drug research or simply increasing its cost? An evidence-based study of the cost for data monitoring in clinical trials

AIM

Procedures for verification of data from clinical studies are intended to maintain reliability for clinical trial results. Guidelines or legislations relating to clinical data management are of limited value and no study has yet demonstrated its effectiveness.

METHOD

Sponsor queries and dual entry procedures from one CRO on three different phase I trials are analysed on content, impact and cost.

RESULT

In this study, sponsor queries and dual entry procedures proved time and cost inefficient in detecting data discrepancies.

CONCLUSION

We advocate a more evidence-based approach for enhancing data integrity throughout the process of clinical data management.

Single-bead, single-molecule, single-cell fluorescence: technologies for drug screening and target validation

According to many current reports, the pharmaceutical business will hit a wall over the next few years. The generic competition is expected to wipe out a double-digit billion-dollar amount from top companies' annual sales between 2007 and 2012 (Wall Street Journal, online, December 6, 2007). The industry's science engine has stalled, new blockbusters are lacking, and patent expirations are a big problem. Also, the U.S. Food and Drug Administration is pulling back on approvals, requesting larger safety studies. Among the different approaches taken throughout the industry to improve productivity and to reduce the attrition rate of compounds in the drug discovery process, an extended application of quantitative biology and biophysical methods is ranked very high. Fluorescence spectroscopy and imaging represented the main detection technologies for assays and screening methods in recent years. Today, label-free detection methods, such as isothermal titration calorimetry, differential scanning calorimetry, tandem mass spectrometry (MS(n)), light scattering, or interferometry, start to provide viable alternative readouts for physicochemical characterization of leads and hit list triaging. However, the multidimensional nature of fluorescence along with its high sensitivity and single-molecule resolution remains an unparalleled source of molecular parameters to extract all different kinds of information on molecules and ligand-protein complexes in solution. Although fluorescence-based methods are currently applied throughout the different stages of the industrial drug discovery process, they are usually applied in an unconnected way. We have developed a fully integrated hit and lead discovery process combining bead-based synthesis and screening methods with confocal fluorescence microspectroscopy. The primary on-bead screening process provides fluorescent ligands that after a multistep characterization process ultimately leads to fully mechanistically characterized cellularly validated binders and inhibitors of target protein interactions. The unlabeled small-molecular inhibitors represent chemical starting points in drug discovery and target validation.

Assessment of anti-metastatic drug efficacy via localization and quantification of ex vivo murine bone tumor load using high-throughput MRI T1 parametric analysis

MRI methods show great utility for assessing the growth of tumors metastasized to bone in clinical studies. However, preclinical MRI methods in rodents do not translate well to high-throughput studies of bone tumors, especially for early-stage tumors typically examined in pharmaceutical discovery efforts. To overcome these limitations, an ex vivo MR T1 parametric mapping method has been developed to measure metastasized bone tumor load in murine long bones. This method has been used to assess the therapeutic efficacy of SU11248, a multi-targeted inhibitor with demonstrated anti-tumor activity and reduction of bone loss, in a murine model of metastasized breast tumor cells. The results show precise localizations of relative tumor loads within the bones and reveal significant differences between SU11248-treated and untreated animal groups. The procedures were optimized for simultaneous, high-throughput parallel image acquisition of MRI data for 30 samples and included an automated segmentation method for image processing. The merits of this T1 parametric mapping method are compared with clinical T1-weighted MRI methods, histopathology and bioluminescence imaging of the same murine bone tumor model.

Serendipitous insights involving nonhuman primates

Serendipity is discussed as a form of controlled chaos, a phenomenon in a class with synchronicity and other actions affecting research in terms of theory versus observation (e.g., "optional stopping"). Serendipity is a fundamental aspect of basic research, a profitable and normal outcome in the context of "informed observation." The serendipitous finding fits into the following pattern: it is unanticipated, anomalous, and strategic. All observations that have meaning must fit into some context in the observer's mind or suggest a revolutionary new context. It is critically important to maintain access to the resources provided by established primate centers and similar laboratories to capitalize in a timely way on serendipitous findings and to benefit from valuable discoveries made in more directly targeted development investments. Examples are given of serendipitous insights gained in experimentation and observation relative to nonhuman primate research, including both broad and narrow topics. Genomics, which uses comparison-based strategies and capitalizes on the DNA sequences of genetic information, presents what might seem the basis for endless serendipity because nonhuman primates are likely to share most genes present in the human genome. Other topics discussed include infant behavior, birth periodicity, leprosy, cystic fibrosis, environmental enrichment, endocrinology, drug development, and the rapidly expanding study of infectious diseases and pathogen-based bioterrorism.

Application of Scintillation Proximity Assay in Drug Discovery

Scintillation proximity assay (SPA), characterized by its speed, sensitivity, reliability, and the fact that no separation step is required, has become an important technique in high-throughput screening (HTS) for new drugs, and for investigating their biological interactions. The SPA technique now plays a key role in HTS, in that it can be used in many assay formats including radioimmunoassays (RIAs), ligand-receptor binding assays, and enzyme assays. The SPA-based enzyme assay is usually designed in three formats corresponding to different enzymes: signal removal format for hydrolytic enzymes, signal addition format for polymerase and transferase enzymes, and product capture format for antibodies, DNA probes, receptors or other specific binding proteins. The use of SPA in RIAs has been facilitated by new carriers, such as membranes that can be configured in various shapes and sizes, allowing the assay to be performed on samples from many sources including tissue, serum, plasma or cells. This review presents the principles of SPA, discusses supporting materials and quenching effects, as well as detailed examples of the latest advances.