October 28, 2018
The increasing availability of artificial intelligence (AI) and deep-learning algorithms able to analyse from many different perspectives the huge amount of R&D data already available in public and private research labs and databases are changing the way drug discovery and development is run. A recent review published in SLAS Discovery and signed by a group of AbbVie’s R&D scientists discusses the many new and emerging approaches to evaluate off-target toxicology from an industrial perspective. Many innovative AI-based companies are also playing an increasing role in supporting traditional pharmaceutical companies in the identification of new, promising therapeutics. We summarise the main features and the many tools discussed in the review and provide some not-exhaustive examples of new business models emerging in the pharmaceutical sector.
The integrated screening paradigm for off-target toxicity
The integrated screening paradigm may support the early identification of off-target toxicology and safety issues, a challenge that is still hard to estimate due to the difficulties to translate observed data from animal to humans and the possible inability to recognise potentially susceptible human subpopulations, argues AbbVie’s group of scientists in the in SLAS Discovery paper.
It is not possible to completely avoid off-target toxicity, especially when small molecules are concerned: an optimised lead may bind to several different targets, a phenomenon which impacts on the observed toxicity profile. Exposures in the in vivo populations represent another bias, according to the review, as many factors might alter this parameter in certain subpopulations of patients.
Gene expression profiling and mapping
Many new analytical tools are available, according to AbbVie’s scientists, to support the elucidation of target and off-target interactions of small molecules, such as for example the L1000 gene expression profiling. The method – born from a collaboration between the MIT’s Broad Institute, Harvard University and Genometry – is based on a high-throughput gene expression assay that measures the mRNA transcript abundance of 978 “landmark” (the “L” in the name) genes from human cells, coupled to the measure of expression of 80 control transcripts chosen for their invariant expression across cell states. Readings are performed on crude lysates of human cells, obtaining an output dataset of expression values for 22,000 genes × 380 samples, suited to be used by machine-learning algorithms and AI-guided drug discovery. The method might be applied, for example, to evaluate the broad impact of the small molecules on cells and identify common and targeted mechanisms of action.
The Broad Institute is also the creator of the Connectivity Map (CMap), a comprehensive catalog of cellular signatures representing systematic perturbation with genetic (reflecting protein function) and pharmacologic (reflecting small molecule function) perturbagens. The library currently contains over 1.5 million gene expression profiles from about 5,000 small molecule compounds and 3,000 genetic reagents, tested in multiple cell types. The database is hosted in the cloud-based infrastructure CLUE (CMap and LINCS Unified Environment), from which researchers can access and manipulate CMap data and integrate them with their own.
L1000 gene expression profiling is also at the base of the National Health Institute (NIH) Library of Integrated Network-Based Cellular Signatures (LINCS) program, an open resource containing assay results from cultured and primary human cells treated with bioactive small molecules, ligands such as growth factors and cytokines, or genetic perturbations. The program aims to better understand the functioning of cell pathways and to support the development of therapies able to restore perturbed pathways and networks.
Phenotypic profiling and CRISPR libraries
The BioMAP human primary cell phenotypic profiling services provided by DiscoverX/Eurofins is another useful tool to determine the efficacy, safety, and mechanism of action of small molecules, say the review’s authors. The system is based on over 60 human primary cell-based models of tissue and disease biology, coupled to a reference benchmark database of more than 4,500 reference compound profiles. Bioinformatic tools associated to the system provide the desired insights.
Screening of CRISPR-generated libraries is another tool complementary to the above mentioned ones; according to the review, this approach allows to study either activating (CRISPRa) or inhibiting (CRISPRi) genes using gene editing. CRISPRa techniques are also useful to assess gain of functions and survival of cells under specific conditions (e.g. the presence of the candidate substance), says an article published in the Journal of Human Genetics, while CRISPRi is a more powerful tool than RNA interference (RNAi) libraries in screening for loss of functions and it can be used also to assess synthetic lethality interactions.
Cellular thermal shift assay mass spectrometry (CETSA-MS) is another recent label-free, biophysical assay based on proteomics useful to evaluate target engagement of a candidate molecule. The method invented at the Swedish Karolinska Institute (which founded the startup Pelago Bioscience to exploit it) allows for the direct measure of ligand-induced changes in protein thermal stability both in living cells and tissues (read here more details).
In vitro ligand binding assays
In vitro panel ligand binding assays can be run using pharmacological targets similar to the one of interest or against off-targets known to be associated with adverse side effects, suggested the SLAS Discovery’s review. According to the recent US regulations on abuse potential of new drugs, assays for neuronal systems related to drug abuse potential and transporters might also be included, suggest authors.
Many assay panels are also commercially available to test kinase activities and interactions with small molecules. More complex is the elucidation of the possible interactions with microtubules and the electron transport chain components: the suggestion in this case is to approach it through routine assessment in advanced mitochondrial cell health assays. Label and label-free technologies can also be used to determine binding with small molecules; the review provide a wide list of both types of methodologies to be used to better assess target deconvolution.