Complementing preclinical safety assessments through genomic analyses

Accepted Manuscript A review article for The Current Opinion in Toxicology. Complementing Preclinical Safety Assessments through Genomic Assessments Parimal Pande, Melissa Giambalvo, Zimei Huang PII:

S2468-2020(18)30048-2

DOI:

https://doi.org/10.1016/j.cotox.2019.01.002

Reference:

COTOX 166

To appear in:

Current Opinion in Toxicology

Received Date: 14 September 2018 Revised Date:

11 January 2019

Accepted Date: 14 January 2019

Please cite this article as: P. Pande, M. Giambalvo, Z. Huang, A review article for The Current Opinion in Toxicology. Complementing Preclinical Safety Assessments through Genomic Assessments, Current Opinion in Toxicology, https://doi.org/10.1016/j.cotox.2019.01.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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A review article for The Current Opinion in Toxicology.

Complementing Preclinical Safety Assessments through Genomic Assessments Parimal Pande1#, Melissa Giambalvo1, Zimei Huang1

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1 Boehringer Ingelheim Pharmaceuticals, Non clinical drug safety, Ridgefield, CT 06877. All authors contributed equally in preparation of this manuscript. # Corresponding Author: [email protected]

Abstract

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During preclinical drug discovery and development, emphasis is placed on accurately identifying risk associated with potential drug-induced toxicity prior to clinical trials. The

toxicology

species,

biomarker

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success of preclinical studies relies on several components including selection of relevant identification

to

determine

target

engagement

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pharmacodynamic effect, translatability of adverse and non-adverse events from animal models to humans, and mechanistic understanding of off-target and histopathological findings. Genomic analyses work in coordination with standard assessments such as histopathology interpretations,

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clinical chemistry findings, soluble protein markers, and physiological readouts to direct and add confidence to the selection of a safe starting dose for first-in-human trials. In this opinion paper we discuss some of the applications, strengths, weaknesses, and potential future directions of

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genomics use in preclinical toxicology.

Keywords: safety assessment; preclinical toxicology; gene expression; target risk assessment; species selection; target engagement

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Summary Figure: Potential applications of genomics throughout drug discovery and preclinical toxicology and the amount of genomic analysis used in the various phases.

1. Introduction

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In the process of drug discovery and development, a candidate drug must achieve several

critical milestones. One of the important phases in this process is preclinical safety assessment, where the focus is on identifying risk associated with drug-induced toxicity prior to clinical trials (1, 2). The acute and sub-chronic studies performed in preclinical development guide decision making regarding potential starting dose for Phase I clinical trials based on adverse event levels in animal models (3, 4). These studies also provide a margin of safety and understanding of super-pharmacological effects since preclinical studies are designed to achieve the maximum 2

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tolerated dose and toxicities could occur at any point in a rather broad dose range (5). There are several factors which impact the success of preclinical studies including selection of relevant rodent and non-rodent species (1, 3, 6), selection of appropriate biomarker(s) to assess target

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engagement or pharmacodynamic effect (7, 8, 9, 10), translatability of adverse and non-adverse findings to humans (11), and mechanistic understanding of off-target and histopathological findings. With these concerns in mind, the use of genomics has proven itself to be an important

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strategic advantage (12). Genomic analyses work in coordination with other assessments such as histopathology readings, clinical chemistry markers, soluble protein markers, and physiological

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readouts to guide the selection of safe starting dose in the clinic (13). Genomic analyses include the compilation of existing datasets from standard toxicological species (rodent, dog, minipig, rabbit, non-human primate, etc.) for justifying selection of appropriate species for safety studies, as well as performing gene expression analysis to de-risk adverse event findings in a mechanistic

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study (12). In this opinion paper we discuss some of the applications, strengths, weaknesses, and potential future directions of genomics in preclinical toxicology. 2. Target Risk Assessment

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The target exploration phase of drug development necessitates understanding potential

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risks associated with a proposed drug target (14). These target assessments are required for upfront evaluation of high impact safety risks, prospective likelihood of off-target effects, and impact of these concerns on the progression of the target through preclinical drug development (15). Target risk assessments can be used for early evaluation of safety by employing nontypical animal models to ensure effective transition of a new drug from research into development. An investigation into target risk assessment will cover the structure and function of a target with emphasis on protein variants and the expression of those variants across species. An

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examination of knockout models is useful in risk assessments and can provide information about mechanism of toxicity due to inhibition of a particular target (16). Potential contents of a target risk assessment are described in Figure 1, although additional topics of interest beyond the areas

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covered here may also be included. Information related to a target can be acquired from a variety of expression databases, functional and knockout information databases, as well as competitive

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intelligence databases such as those pertaining to clinical trials (Table 1).

Figure 1: Target risk assessment areas of interest for a given target.

Function, Structure (incl. homology)

Literature Search

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Expression

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GTEx Portal58 Aceview/NHPRTR59 ProteomicsDB60 RNA-Seq Atlas61 Human Protein Atlas62

Uniprot63 NCBI64 ENSEMBL65 MGI66 OMIM67 COSMIC68

PubMed69 Ovid70 IPA71 Insight meme72

Competitive Intelligence Citeline73 Trialtrove74 Integrity75 TABS76 Clinicaltrail.gov77 NextBio78

Table 1: List of databases organized by intended use.

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3. Relevant Species Selection Although the selection of preclinical toxicology species is relatively standard for new chemical entities (NCEs), the selection of a pharmacologically relevant species for assessment of

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toxicity relating to new biological entities (NBEs) is more strategic (17). International Conference on Harmonization (ICH) guidelines suggests various criteria for selection of species and design of experiments (6). Typically two species are used, a rodent and a non-rodent, to

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maximize the predictability of adverse events in first-in-human trials (3). Relevant species are selected based on expression of candidate drug target, optimal protein sequence homology, and

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demonstration of similar target binding and functional cross reactivity to human (18). More specifically, species selection for preclinical trials largely revolves around knowledge of the target in consideration and the need for that target to be pharmacologically relevant in the animal model selected (13). The availability of genome sequence information for candidate species is a

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significant factor in determining species relevance. For an animal model to be deemed appropriate, the target must be expressed and ideally is functionally active (19). A well-known example of the effect of expression disparity between human and non-human primates is the

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differential CD28 expression on CD4+ effector memory T cells, leading to the failure of TGN1412 in first-in-human clinical trials due to resulting cytokine storm in healthy human

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volunteers (20). Thorough evaluation of gene expression pathways in human vs. chosen toxicology species sheds light on potential adverse events such cytokine release or shifting immune cell subsets. Previously, technologies such as microarray or qPCR were used to perform target protein and pathway analysis. With recent developments in cutting edge technology such as next generation sequencing, it is becoming easier, cheaper and faster to obtain global gene expression data, improving relevant toxicology species identification (21). Global expression data can also be used for identification of orthologues/paralogues and immunogenicity which 5

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may be of particular importance when choosing the appropriate species prior to study design or when evaluating the suitability of chosen species following a preclinical adverse event. In the event that pharmacologically relevant preclinical species cannot be chosen, a

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surrogate approach may be necessary. A surrogate method can include knock-out, knock-in or transgenic models where homology assessment, target expression, and pathway analysis can be performed to determine suitability of the chosen surrogate (22). Alternatively, a cross-reacting

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antibody can be developed for an animal model to evaluate the safety profile of a drug (18). Taking all options into consideration, the use of gene expression profiling when performing

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species selection is the first of many critical steps in preclinical study design.

4. Pharmacodynamic Effect/ Target Engagement

During early drug discovery, toxicology studies are performed on cells or animal models

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of the disease being investigated to discover new targets based on phenotypic rescue (23). The target protein of interest is usually highly expressed in disease conditions when compared to healthy animals in preclinical studies or persons in Phase I or Phase II trials (17, 24). In these

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early studies, pharmacodynamic effect can be measured by alleviation of disease symptoms or physiological parameters (23). Preclinical toxicology studies are designed to measure tolerability

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and estimate toxicity associated with new drug molecules in healthy animals to develop informed risk for the start of clinical studies (3, 25). After selection of appropriate species for preclinical evaluation, the determination of pharmacodynamic response becomes a challenge, since preclinical animals may express a lower level or complete lack of target protein expression and/or may not demonstrate the expected physiological response (17). Monoclonal antibodies, for example, typically show very low to no toxicity due to high specificity for their target protein and rarely show off target toxicities, unlike small molecules (26). In such cases, gene expression 6

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analysis may be capable of providing valuable complementary information on pharmacodynamic effect. Pharmacokinetic analysis may show drug exposure across doses and over time, but analysis of genes downstream of a drug target, or analysis of genes modulated by the target

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protein and/or the target’s receptor, is able to provide confirmation of relevant species selection and pharmacodynamic effect of a new drug. The relationship is described, generally, in Figure 2 where positive exposure is demonstrated by pharmacokinetic analysis and corroborated by gene

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expression data, even in the absence of histopathology findings. In the early stages of drug discovery, global gene expression analysis can be paired with other physiological correlations to

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narrow down a target list. However, in preclinical toxicology, selection of genes is literature

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driven and based on limited datasets (12).

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Figure 2: Abstract data of control, low, mid, and high dosed animals. Exposure data indicates target engagement of varying degrees in all dosed groups (Green=control, blue=low, orange=mid, red=high). Histopathology grades (0=no finding, 1=minimal finding) are negative or inconclusive. Gene expression data shows drug-related effect, which is generally doseresponsive, adding confidence to exposure data.

5. Biomarker Identification An essential component to the success or failure of a drug candidate in preclinical trials is

the availability and reliability of biomarkers to detect and monitor its potential toxicity (13). A valid biomarker could be a protein, mRNA, clinical chemistry marker or any number of physiological findings used in a fit-for-purpose assessment of treatment effect (27, 28). A 7

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biomarker can be classified as predictive or injury-driven and ideally is minimally invasive, sensitive and specific. The development of multi-omics and the use of statistical programming have gained importance in the detection of drug-induced toxicity and can be used to identify

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novel biomarkers of drug-related injury as well as provide context to the pathway(s) surrounding biomarker changes (29). Recently, much attention has been paid to detecting organ-specific toxicity in order to advance the translatability of findings between the preclinical and clinical

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stages, as well as to improve prediction of specific organ toxicity in early development (30). Drug-related increases or decreases in biomarkers in the periphery need to be attributed to a

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specific organ to fully understand mechanism of action (31, 32). Soluble protein biomarkers are routinely used as candidates for safety assessment (8, 9); however it is difficult to associate soluble protein changes in circulation with specific organ toxicity. In recent years, advances in genomic technologies have led to circulating mRNAs and miRNAs being investigated as

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potential biomarkers (33, 34). The attractiveness of mRNAs/miRNAs as biomarkers is due to their tissue-specific nature, as well as their release into circulation following a drug-related toxicity event (35). Additionally, miRNAs have the potential to drastically improve

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translatability to the clinic due to their conserved nature across species (28). The future of miRNAs and mRNAs as predictive biomarkers of disease and organ toxicity will likely expand

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as improving technologies further our ability to characterize the association and establish robust analysis methods (35).

6. Future Directions

The application of genomics to preclinical toxicology has advanced significantly and the landscape is continually changing. New technologies and algorithms are being invented to achieve precise answers in minutes and with minimal investment. In drug discovery, gene 8

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expression analysis is extensively used to recognize new targets for drug development, (36) but in preclinical safety assessments, expression analysis is applied infrequently (37). Gene expression analysis provides a snapshot of genes or pathways being regulated at that particular

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time point, however actual expression at the time of injury and expression changes due to compensatory homeostatic mechanisms in various cell types may be different (38). Additionally, gene to protein translation is not always a direct correlation rather it depends upon various

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scenarios such as short-term adaptation, long-term or steady state changes and gene regulation (39, 40). In order to gain a comprehensive understanding of the mechanism of action, a time

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course study is required where samples collected at different time points can provide information about pathways being modulated. Whole transcriptome analysis is beneficial in such scenarios, but in cases where the type of injury is well documented in the literature, a targeted panel with qPCR or a mid-throughput technique such as Nanostring® may be beneficial. Further

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investigation of tissue-specific pathway gene signatures following drug-induced-injury can be obtained from public databases such as Open TG Gates, CTD and TOXsIgN (41, 42, 43). The Food and Drug Administration (FDA) is leading efforts such as Sequencing Quality Control

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(SEQC) (44) to gain a better understanding of next generation RNA-sequencing techniques and common practices. These efforts highlight how industry and academia have been applying RNA-

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Seq analysis in the investigation of mechanisms of action. Despite these efforts, maintaining current and streamlined data analysis practices is difficult, as new models of differential gene expression analysis are continuously emerging. It becomes the role of the bioinformatician to choose the most appropriate method of data analysis based on expertise and proposed query. A significant caveat in global gene expression analysis is the inherent averaging of the expression of genes across a variety of cell types present in a given tissue (45). For example, the 9

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expression data collected from a liver sample may be composed of hepatocytes, Kupffer cells, hepatic stellate cells, etc. (46). In addition, collection of tissue at the exact site of histological change vs. some distance away from the observed change, may affect pathway analysis (47). To

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overcome these challenges, techniques such as single cell sequencing (SCS) and/or laser capture microdissection are valuable (48, 49). SCS combined with RNA-Seq provides information about expression changes within a single cell type. With the help of advanced software(s) and open

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source algorithms, it is possible to tease out a hypothesized mechanism of action by constructing a causal network (50). The expression values per cell type may also be applied to in silico

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modeling techniques, where injuries could be predicted computationally.

The growing availability of large and publically available datasets has made the use of in silico models an increasingly attractive avenue for predicting toxicity (51). Moreover, in silico modeling has the potential to reduce the use of animal models, which would be attractive for

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ethical, financial, and efficiency reasons (52, 53, 54, 55). In pharmaceuticals, in silico modeling is primarily used in lead identification or later in development during safety assessments. Using SCS data, tissue expression data, literature, and soluble protein expression data, a comprehensive

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in silico predictive toxicology model can be developed. As shown in Figure 3, this model can be

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applied to determine therapeutic window, select protein biomarkers to measure toxicity, and compile a candidate gene list for assessment of toxicodyamic effect (56). The model can also be applied to predict organ injury and/or species-specific adverse events. Although making predictions via in silico models has been met with measured success, the inherent weakness of an in silico approach is the reliance on the accuracy of datasets, as well as the model that the data is being applied to (57).

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Figure 3: Workflow showing use of single cell sequencing data in development of predictive toxicity modeling and construction of mechanistic hypothesis.

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7. Discussion

The use of genomics has proven to be an effective tool to support preclinical development. When choosing the appropriate toxicology species, genomics has the potential to add confidence that the test animal will express the drug target, has the appropriate sequence homology, and can demonstrate pharmacodynamic effect (13, 18, 19). Gene expression can also

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be paired with other endpoints, such as protein and small molecule based biomarkers, to demonstrate target engagement and pharmacodynamic effect in non-diseased preclinical animal models. In the recurring search for predictive and accurate biomarkers to measure drug-related

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injury, the use of multi-omics and statistical programming have helped shape understanding of pathway variations and organ-specific toxicity (29). Although traditionally speaking, predictive

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biomarkers have relied heavily on soluble proteins, there is great potential in mRNAs and miRNAs as measurements of drug-related toxicity due to their tissue-specific nature as well as typically being conserved across species, bridging the divide between preclinical and clinical trials (28, 35). In the past, the interpretation of gene expression data was limited by the intrinsic averaging of signal from all cells in a given sample, however single cell sequencing provides an opportunity to tease out expression of a specific cell type, improving accuracy of pathway

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change interpretation, as well as providing more exact information to apply to in silico modeling techniques (48, 51). Going forward, the pressure to improve accuracy when identifying, predicting and understanding human relevance of drug-induced preclinical toxicity will continue.

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It would be to the benefit of all drug-discovery institutions to apply genomics in their preclinical work in order to keep up. 8. Declaration of interest

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We wish to confirm that there are no known conflicts of interests associated with this publication

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and the authors did not receive any financial support to influence their opinion or outcome. 9. Funding

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No funding or financial support was received for this work.

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