In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Connecting Clinical Phenotypes to Molecular Knowledge
2.2. Data Integration
2.3. Characterization of Relationships
2.4. The Collection of Relationships
2.5. Presented Validation Examples
2.5.1. Prospective Prediction of Side Effects
2.5.2. Hypothesis Testing for Improved Use of Drugs
2.6. Benchmarking
- (a)
- To ensure specificity we primarily considered reactions described at MedDRA level 4. In many cases AEs mentioned exactly a certain term as reported in the original dataset (e.g., ‘Tachycardia’), whereas other times an effect could only be captured in AEs via higher MedDRA level descriptions (e.g., ‘salivation’ and ‘Salivary gland disorders NEC’). In some other cases this strategy led to the identification of reaction terms too specific and with scant occurrence in AEs (e.g., ‘Hypophosphatemia’ or ‘Hypocalcaemia’).
- (b)
- We searched only for reactions: The physiological effects expected to occur when compounds hit the listed targets of [15] may be characterized with increased PRRs in AEs, however sometimes they may only reflect a condition and not an adverse reaction.
- (c)
- We searched for up to two synonyms per case: Sometimes the same effect may have been declared via its synonyms or in similar spelling (e.g., ‘anemia’ and ‘anaemia’). In other cases, alternative names were searched to describe generic phrases of the original dataset (e.g., both ‘hypertension’ and ‘hypotension’ were searched for the originally termed effect ‘blood pressure changes’).
- (d)
- We also did not consider effects of proteins that were not targets or had no drugs reported in AEs.
2.7. Comparison of Target-Reaction Signals
2.8. Data Availability
3. Results
3.1. Phenotypic Profiling of Human Target Proteins
3.2. Using the Approach: Examples and Perspectives of Analytical Strategies
3.3. Validation Results
- Prospective prediction of drug side effects. We asked whether it might be possible to predict AE-profiles of novel drugs. Examining the reaction profiles of tyrosine kinase inhibitors (TKIs) revealed that Sorafenib is more strongly associated with dermatological reactions than Sunitinib (Supplementary Table S2). Indeed, skin-related AEs are a common form of side-effects observed in patients treated with TKIs [16,17,21,22]. However, while these two TKIs share a common target-inhibition profile [23,24,25,26], Sorafenib additionally inhibits BRAF. We capitalized then on the Sorafenib-specific side-effect difference to hone in on the BRAF-specific clinical effects and hypothesized that the reaction profiles of other BRAF inhibitors may also include skin-related AEs. To test whether we could have predicted this relationship for the BRAF-specific inhibitor Vemurafenib, we used a data slice of all patient-cases reported prior to Vemurafenib’s FDA-approval in August 2011. Through this prospective-retrospective analysis we found that BRAF perturbation was strongly related to dermatologic reactions (Supplementary Figure S2)—thus predicting the primary side-effects observed in Vemurafenib’s phase-3 trial [27] (NCT01006980). Indeed, predicted skin-related AEs are consistent with BRAF pathway findings from recent studies and other independent work [28,29,30,31]. Importantly, this clinical effect is also included in Vemurafenib’s FDA-label [32].
- Rational prediction of combination therapies. We also assessed whether we could identify combinations of targets that may improve therapeutic outcomes in patients. We reasoned that AE outcomes and in particular “death rate”, might be used as surrogate marker for treatment efficacy. Using prior knowledge about the association between biobehavioral stress and tumorigenesis [12,13], we investigated whether perturbation of beta-adrenergic receptor (BAR) function in cancer patients might result in lower patient mortality. We examined the phenotypic consequences of BAR activity modulation by comparing two similar cohorts of skin cancer patients—one arm with and one without inhibition of BAR activity (Supplementary Figure S3). In absence of BAR antagonism, reported deaths occurred in 23.6% cases, whereas with BAR inhibition deaths were reported in 18.4% cases. These results suggested that co-medication of BAR blockers may be associated with reduced mortality of skin cancer patients, and were supported by subsequent in vitro and in vivo studies which demonstrated the role of BAR in tumor growth and stress-response signaling via SRC activation in cancer cells. Further analysis revealed that mortality was reduced across major cancer types in patients where BAR signaling is inhibited with beta-blockers and identified BAR inhibition as a potential combinatorial route to anti-cancer treatment [33]. In support of these observations, a recent phase-2 pilot-study (NCT01265576) examining clinical effects of beta-blocker usage in hepatocellular carcinoma (HCC) patients with VT-122 (combination of the non-selective beta-blocker Propranolol and the COX2-selective Etodolac) demonstrated potential anticancer effects when co-administered with Sorafenib by increasing therapy duration and overall survival, as compared to HCC patients treated with Sorafenib alone [34]. Our example study emphasizes the importance of clinical phenotype profiling at the level of target perturbations, but also provides a potentially novel approach to drug repositioning, via rational design of combination therapies.
3.4. Benchmarcking and Comparison
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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AE Cases | Event (E) | Not E | Totals |
---|---|---|---|
D (T) | a | b | A + b |
Not D (T) | c | d | C + d |
Totals | a + c | b + d | N = a + b + c + d |
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Soldatos, T.G.; Taglang, G.; Jackson, D.B. In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data. High-Throughput 2018, 7, 37. https://doi.org/10.3390/ht7040037
Soldatos TG, Taglang G, Jackson DB. In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data. High-Throughput. 2018; 7(4):37. https://doi.org/10.3390/ht7040037
Chicago/Turabian StyleSoldatos, Theodoros G., Guillaume Taglang, and David B. Jackson. 2018. "In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data" High-Throughput 7, no. 4: 37. https://doi.org/10.3390/ht7040037