Current Topics in Nonclinical Drug Development

Volume 1

Pritam S. Sahota, Philip Bentley, Zbigniew Wojcinski, Pritam S. Sahota, Philip Bentley, Zbigniew Wojcinski

• Editorial: CRC Press
• Año: 2020
• ISBN:
  9780429648496
• Edición: 1
• Categoría: Medicine
• Categoría: Pharmacology

The inaugural volume in the Current Topics in Nonclinical Drug Development Series explores the critical issues and current topics in nonclinical drug development. This first volume covers individual topics and strategies in drug development from compound characterization to drug registration. Written by a variety of experts in the field, recent and rapid advances in technologies and associated changes in regulatory guidance are discussed.

Additional features include:

• Deals with day-to-day issues in study design, evaluation of findings, and presentation of data.

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• Explains new approaches in the development of medical devices.

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• Includes dedicated chapters on the use of bioinformatics in drug development.

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• Addresses strategies for photosafety testing of drugs.

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Current Topics in Nonclinical Drug Development, Volume I will aid toxicologists, toxicologic pathologists, consultants, regulators, Study Directors, and nonclinical scientists dealing with day-to-day issues in study design, evaluation of findings, and presentation of data. In addition, the book will be a valuable reference for academicians and graduate students pursuing research related to nonclinical drug development.

Chapter 1

Bioinformatics/Impact of Computational Biology for Molecular Safety Assessment during Drug Development

Juliane Perner, Megumi Onishi-Seebacher, Alberto Del Rio Espinola, Elaine Tritto, Philippe Couttet, Rémi Terranova and Jonathan Moggs

Novartis Institutes for BioMedical Research

1.1 Introduction

1.1.1 Safety Genetics
Applications

1.1.2 Safety “Omics” Applications

1.2 Practical Computational Biology Considerations

1.3 Future Directions and Challenges

References

1.1 Introduction

Established toxicology disciplines, such as the in silico prediction of adverse events associated with chemical structures, in vitro secondary pharmacology screening for off-targets associated with adverse drug reactions, and toxicologic pathology, are being revolutionized by computational approaches. The careful curation of historical data, and their integration into large-scale databases, allows the application of innovative machine learning and data mining approaches (Dey et al., 2018; Sanz et al., 2017; Steger-Hartman and Pognan, 2018; Clarke et al., 2018), resulting in automated, reproducible, and objective evaluations and predictions of toxicological events.

The advent of large-scale mammalian genome sequencing and the development of genome-wide “omic” profiling technologies in the late 1990s, in particular transcriptomics, offered the opportunity to screen simultaneously the state of thousands of molecular entities of a biological system. The analysis and successful interpretation of these large molecular data sets required new computational biology approaches to be adopted within nonclinical drug safety sciences (Nuwaysir et al., 1999; Moggs, 2005; Fielden and Kolaja, 2006; Gant, 2007; Afshari, 2011). In parallel, there has been a paradigm shift in our understanding of the genome regulatory and functional landscapes (Maurano et al., 2012; ENCODE Project Consortium, 2012; Roadmap Epigenomics Consortium et al., 2015), resulting in an increased need to integrate genetic, epigenetic, and transcriptomic profiles in order to fully elucidate the mechanistic basis of drug-induced molecular and phenotypic effects.

The rapid evolution of bioinformatics applications in toxicology, together with game-changing advances in molecular profiling technologies, such as deep sequencing, has led to computational biology becoming a core component of toxicology study design, analysis, and interpretation (Figure 1.1). Furthermore, a number of important principles have been established to improve the interpretation and usefulness of molecular toxicology data for future reuse and meta-analysis, including (1) the importance of functional/phenotypic anchoring of drug-induced molecular responses (Paules, 2003; Qin et al., 2016; Stiehl et al., 2017); (2) the need to generate reference genomes for toxicology species; (3) the establishment of guidelines and standards for reliability and robustness of genomic technologies that support regulatory decision-making (ICH E15, 2007; Xu et al., 2016); and (4) the elaboration of guiding principles on data management and stewardship (e.g., findability, accessibility, interoperability, and reusability; FAIR; Wilkinson et al., 2016).

Figure 1.1 Computational Biology Approaches for Mechanistic Molecular Toxicology. (a) The integration, analysis, visualization, and phenotypic anchoring of molecular, biochemical, and cellular data derived from nonclinical toxicology models and clinical safety studies can be facilitated through computational biology approaches and represent an iterative process that involves close collaboration between data scientists and subject matter experts. (b) Complex molecular data from a specific study endpoint (e.g., tissue-specific transcriptomic profiling +/− drug treatment) can be interpreted at different biological levels of complexity through integration with prior knowledge and databases. Meta-analysis of large-scale molecular data resources can also provide an unbiased and holistic view of drug effects spanning multiple modes of action and phenotypes and providing the potential for discovery of diagnostic or predictive molecular signatures.

Here, we outline some current computational biology applications in drug safety assessment, illustrating the diversity of tools and resources that are being deployed as well as some of the current challenges and future opportunities in this rapidly evolving field.

1.1.1 Safety Genetics Applications

A wealth of computational tools and resources, many of which can be easily accessed and explored via intuitive web-based browsers, enable biologists to navigate mammalian genome structure, genetic variations, and associated genome functions. These powerful resources enable molecular safety scientists to evaluate the potential impact of germline and somatic genetics on drug development programs including their influence on protein structure-based drug design, the selection of appropriate nonclinical model systems, and assessing the potential for drug target–associated adverse events.

Recent advances in genome resources for animal species that are used in pharmacology and toxicology studies provide a powerful opportunity to systematically optimize species and strain selection through comparative analysis of genetic variation in drug targets and off-targets between animals and humans (Kronenberg et al., 2013; Bhoumik et al., 2017). Such analyses can be extended to optimize the selection of pharmacologically relevant animal strains or species relative to drug target genetic variation that is observed in specific human patient populations. Furthermore, elegant panels of mouse strains have been developed to model human genetic diversity (e.g., the Collaborative Cross; Churchill et al., 2004; http://csbio.unc.edu/CCstatus/index.py) including susceptibility to acetaminophen-induced liver injury and doxorubicin-induced cardiotoxicity (Harrill et al., 2009; Zeiss et al., 2019).

The potential functional consequences of apparent strain- or species-specific genetic variants need to be carefully considered in the context of drug–macromolecule interactions (including small molecule drugs, therapeutic nucleic acids, and biotherapeutics) and ideally validated through follow-up DNA or RNA sequencing together with in vitro biochemical/cellular and in vivo functional assays. Similarly, when transgenic animals are used for safety assessment applications, the molecular characterization of transgene copy number and genomic location represents a critical step for the interpretation of phenotypic effects, particularly for animal models generated by microinjection (De Vree et al., 2014; Goodwin et al., 2019).

A distinct and emerging application for safety genetics is to derisk potential off-target effects associated with viral vector– and gene editing–based cell and gene therapies through genome-wide integration site and/or fidelity analyses. Numerous in silico algorithms, molecular assays, and bioinformatic workflows have been reported for such analyses (Sherman et al., 2016; Berry, 2016; Afzal, 2017; Tsai et al., 2017; Cameron, 2017; Giannoukos, 2018; Lazzarotto et al., 2018). Important factors to consider for candidate off-target genome disruptions include the potential for functional consequences (e.g., oncogenic potential) and also the potential influence of human genetic variation on the fidelity of such therapies, both of which can be initially assessed in silico using existing genome resources.

The availability of extensive human and animal genotype–phenotype resources and associated molecular, biochemical, and cellular databases are also enabling systematic derisking of drug target (and drug off-target) modulation (Roberts, 2018; Table 1.1). This approach is exemplified by the evaluation of potential associations between drug target genotypes and tumorigenic phenotypes based on nonclinical genetic models (cell- and animal-based) and human cancer cell and tissue genome resources (Moggs et al., 2016; Fielden et al., 2018). Genotype–phenotype assessments are equally applicable to the exploration of drug target/off-target association with other toxicities (Diogo et al., 2018; Nguyen et al., 2019), in particular where robust pharmacogenetic data are available (Wei, 2012; Collins et al., 2016; Cook et al., 2018; Cacabelos et al., 2019).

Table 1.1 Illustrative Public Domain Mammalian Genome Resources That Have Been Leveraged for Enhanced Molecular Drug Safety Assessment

Safety Genetics

GRASP, Genome-Wide Repository of Associations Between SNPs and Phenotypes; https://grasp.nhlbi.nih.gov

HGMD, Human Gene Mutation Database; www.hgmd.cf.ac.uk

IntOGen, Integrative Onco Genomics; www.intogen.org

OMIA, Online Mendelian Inheritance in Animals; http://omia.angis.org.au/

COSMIC, catalog of somatic mutations in cancer; http://cancer.sanger.ac.uk/cosmic

OMIM, Online Mendelian Inheritance in Man; www.omim.org

TCGA, The Cancer Genome Atlas; www.cbioportal.org https://tcga-data.nci.nih.gov/tcga/

CCLE, Cancer Cell Line Encyclopedia; www.broadinstitute.org/ccle/home

Safety “omics”

Gene Expression Omnibus (GEO); https://www.ncbi.nlm.nih.gov/geo/

e!Ensembl Comparative Genomics; https://www.ensembl.org/info/genome/compara/index.html

National Center for Biotechnology Information (NCBI); https://www.ncbi.nlm.nih.gov/

Integrated System for Motif Activity Response Analysis (ISMARA); https://ismara.unibas.ch/mara/

Integrative Genomics Viewer (IGV); https://software.broadinstitute.org/software/igv/

Open TG-GATEs: a large-scale toxicogenomics database; http://toxico.nibio.go.jp/english/index.html

ENCODE: Encyclopedia of DNA Elements; https://www.encodeproject.org/

The Human Protein Atlas; https://www.proteinatlas.org/

The Genotype-Tissue Expression (GTEx) project; https://gtexportal.org/home/

NIH Roadmap Epigenomics Mapping Consortium; http://www.roadmapepigenomics.org/

EMBL-EBI Single-Cell Expression Atlas; https://www.ebi.ac.uk/gxa/sc/home

The Gene Ontology Resource; http://geneontology.org/

Many of the computational biology tools and resources for the aforementioned safety genetics applications are relatively easy to access for nonspecialists. However, important factors to consider include the interpretation and relative weighting given to reported/observed genetic variants (in particular, the statistical rigor applied to available data), the need to regularly integrate updated genome databases, the need to map and model the structural consequences of genetic variants on drug–macromolecular interactions, and the need to design appropriate in vitro and/or in vivo assays to evaluate potential functional consequences. Ideally, candidate genetic variants should be shown to correlate with “proximal” molecular markers in relevant pathways and also be robustly statistically associated with the “distal” clinical phenotype.

1.1.2 Safety “Omics” Applications

“Omics” technologies represent a powerful approach for the characterization and interrogation of biological samples by measuring the status (i.e., amount, activity, and/or spatial distribution) of thousands of molecular, biochemical, and cellular entities (e.g., genetics, transcriptomics, epigenomics, proteomics, phosphorylomics, metabolomics, lipidomics, cellomics). These large-scale molecular readouts can be leveraged to guide the selection of appropriate nonclinical models for pharmacology and toxicology studies, to enable the molecular classification of morphologic and/or physiologic alterations elicited by a ...