Delivering Robust Candidates to the Drug Pipeline through Computational Analysis of Arrayed CRISPR Screens

Metabolism-driven in vitro/in vivo disconnect of an oral ERɑ VHL-PROTAC

Targeting the estrogen receptor alpha (ERα) pathway is validated in the clinic as an effective means to treat ER+ breast cancers. Here we present the development of a VHL-targeting and orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of ERα. In vitro studies with this PROTAC demonstrate excellent ERα degradation and ER antagonism in ER+ breast cancer cell lines. However, upon dosing the compound in vivo we observe an in vitro-in vivo disconnect. ERα degradation is lower in vivo than expected based on the in vitro data. Investigation into potential causes for the reduced maximal degradation reveals that metabolic instability of the PROTAC linker generates metabolites that compete for binding to ERα with the full PROTAC, limiting degradation. This observation highlights the requirement for metabolically stable PROTACs to ensure maximal efficacy and thus optimisation of the linker should be a key consideration when designing PROTACs.

Characterization of drug effects on cell cultures from phase-contrast microscopy images

In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

CRISPR Screens in Synthetic Lethality and Combinatorial Therapies for Cancer

Cancer is a complex disease resulting from the accumulation of genetic dysfunctions. Tumor heterogeneity causes the molecular variety that divergently controls responses to chemotherapy, leading to the recurrent problem of cancer reappearance. For many decades, efforts have focused on identifying essential tumoral genes and cancer driver mutations. More recently, prompted by the clinical success of the synthetic lethality (SL)-based therapy of the PARP inhibitors in homologous recombinant deficient tumors, scientists have centered their novel research on SL interactions (SLI). The state of the art to find new genetic interactions are currently large-scale forward genetic CRISPR screens. CRISPR technology has rapidly evolved to be a common tool in the vast majority of laboratories, as tools to implement CRISPR screen protocols are available to all researchers. Taking advantage of SLI, combinatorial therapies have become the ultimate model to treat cancer with lower toxicity, and therefore better efficiency. This review explores the CRISPR screen methodology, integrates the up-to-date published findings on CRISPR screens in the cancer field and proposes future directions to uncover cancer regulation and individual responses to chemotherapy.