Improving drug safety with a systems pharmacology approach

Application of an Integrative Drug Safety Model for Detection of Adverse Drug Events Associated With Inhibition of Glutathione Peroxidase 1 in Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic Obstructive Pulmonary Disease is characterised by declining lung function and a greater oxidative stress burden due to reduced activity of antioxidant enzymes such as Glutathione Peroxidase 1.

OBJECTIVES

The extent to which drugs may contribute to this compromised activity is largely unknown. An integrative drug safety model explores inhibition of Glutathione Peroxidase 1 by drugs and their association with chronic obstructive pulmonary disease adverse drug events.

METHODS

In silico molecular modelling approaches were utilised to predict the interactions that drugs have within the active site of Glutathione Peroxidase 1 in both human and bovine models. Similarities of chemical features between approved drugs and the known inhibitor tiopronin were also investigated. Subsequently the Food and Drug Administration Adverse Event System was searched to uncover adverse drug event signals associated with chronic obstructive pulmonary disease.

RESULTS

Statistical and molecular modelling analyses confirmed that the use of several registered drugs, including acetylsalicylic acid and atenolol may be associated with inhibition of Glutathione Peroxidase 1 and chronic obstructive pulmonary disease.

CONCLUSION

The integration of molecular modelling and pharmacoepidemological data has the potential to advance drug safety science. Ongoing review of medication use and further pharmacoepidemiological and biological analyses are warranted to ensure appropriate use is recommended.

Application of a patient-centered reverse translational systems-based approach to understand mechanisms of an adverse drug reaction of immune checkpoint inhibitors

Immunotherapy became a key pillar of cancer therapeutics with the approvals of ipilimumab, nivolumab, and pembrolizumab, which inhibit either cytotoxic T‐lymphocyte antigen‐4 (CTLA‐4) or programmed death‐1 (PD‐1) that are negative regulators of T‐cell activation. However, boosting T‐cell activation is often accompanied by autoimmunity, leading to adverse drug reactions (ADRs), including high grade 3–4 colitis and its severe complications whose prevalence may reach 14% for combination checkpoint inhibitors. In this research, we investigated how mechanistic differences between anti‐CTLA‐4 (ipilimumab) and anti‐PD‐1 (nivolumab and pembrolizumab) affect colitis, a general class toxicity. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases for hypothesis generation regarding the underlying molecular mechanisms causing colitis. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. We verified that the anti‐CTLA‐4 drug is associated with an approximately three‐fold higher proportional reporting ratio associated with colitis than those of the anti‐PD‐1 drugs. The signal of the molecular mechanisms, including signaling pathways of inflammatory cytokines, was statistically insignificant to test the hypothesis that the severer rate of colitis associated with ipilimumab would be due to a greater magnitude of T‐cell activation as a result of earlier response of the anti‐CTLA‐4 drug in the immune response. This patient‐centered systems‐based approach provides an exploratory process to better understand drug pair adverse events at pathway and target levels through reverse translation from postmarket surveillance safety reports.

A case study of a patient-centered reverse translational systems-based approach to understand adverse event profiles in drug development

Adverse drug reactions (ADRs) of targeted therapy drugs (TTDs) are frequently unexpected and long‐term toxicities detract from exceptional efficacy of new TTDs. In this proof‐of‐concept study, we explored how molecular causation involved in trastuzumab‐induced cardiotoxicity changes when trastuzumab was given in combination with doxorubicin, tamoxifen, paroxetine, or lapatinib. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases (such as UniProt and Reactome), for hypothesis generation regarding the underlying molecular mechanisms causing cardiotoxicity. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. Literature search was performed to compare the established hypotheses to published experimental findings. We found that the combination therapy of trastuzumab and doxorubicin may affect mitochondrial dysfunction in cardiomyocytes through different molecular pathways such as BCL‐X and PGC‐1α proteins, leading to a synergistic effect of cardiotoxicity. We found, on the other hand, that trastuzumab‐induced cardiotoxicity would be diminished by concomitant use of tamoxifen, paroxetine, and/or lapatinib. Tamoxifen and paroxetine may cause less cardiotoxicity through an increase in antioxidant activities, such as glutathione conjugation. Lapatinib may decrease the apoptotic effects in cardiomyocytes by altering the effects of trastuzumab on BCL‐X proteins. This patient‐centered systems‐based approach provides, based on the trastuzumab‐induced ADR cardiotoxicity, an example of how to apply reverse translation to investigate ADRs at the molecular pathway and target level to understand the causality and prevalence during drug development of novel therapeutics.

Mechanistic Insight into the Binding and Swelling Functions of Prepeptidase C-Terminal (PPC) Domains from Various Bacterial Proteases

The bacterial prepeptidase C-terminal (PPC) domain can be found in the C termini of a wide variety of proteases that are secreted by marine bacteria. However, the functions of these PPC domains remain unknown due to a lack of systematic research. Here, the binding and swelling abilities of eight PPC domains from six different proteases were compared systematically via scanning electron microscopy (SEM), enzyme assays, and fluorescence spectroscopy. These PPC domains all possess the ability to bind and swell insoluble collagen. PPC domains can expose collagen monomers but cannot disrupt the pyridinoline cross-links or unwind the collagen triple helix. This ability can play a synergistic role alongside collagenase in collagen hydrolysis. Site-directed mutagenesis of the PPC domain from Vibrio anguillarum showed that the conserved polar and aromatic residues Y6, D26, D28, Y30, W42, E53, C55, and Y65 and the hydrophobic residues V10, V18, and I57 played key roles in substrate binding. Molecular dynamic simulations were conducted to investigate the interactions between PPC domains and collagen. Most PPC domains have a similar mechanism for binding collagen, and the hydrophobic binding pocket of PPC domains may play an important role in collagen binding. This study sheds light on the substrate binding mechanisms of PPC domains and reveals a new function for the PPC domains of bacterial proteases in substrate degradation.IMPORTANCE Prepeptidase C-terminal (PPC) domains commonly exist in the C termini of marine bacterial proteases. Reports examining PPC have been limited, and its functions remain unclear. In this study, eight PPCs from six different bacteria were examined. Most of the PPCs possessed the ability to bind collagen, feathers, and chitin, and all PPCs could significantly swell insoluble collagen. PPCs can expose collagen monomers but cannot disrupt pyridinoline cross-links or unwind the collagen triple helix. This swelling ability may also play synergistic roles in collagen hydrolysis. Comparative structural analyses and the examination of PPC mutants revealed that the hydrophobic binding pockets of PPCs may play important roles in collagen binding. This study provides new insights into the functions and ecological significance of PPCs, and the molecular mechanism of the collagen binding of PPCs was clarified, which is beneficial for the protein engineering of highly active PPCs and collagenase in the pharmaceutical industry and of artificial biological materials.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

Target-Adverse Event Profiles to Augment Pharmacovigilance: A Pilot Study With Six New Molecular Entities

Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse‐event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F‐measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.

Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions

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All software assessed could dock Abacavir back into the risk allele structure but not always predict the exact binding mode.

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Most docking software assessed can distinguish between risk and control alleles.

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Docking performance can be degraded by using a homology model.

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Receptor flexibility can negatively affect the docking performance for complex HLA examples.

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Using AutoDockFR cannot compensate for the added difficulty of docking to the unbound target.

Adverse drug reactions have been linked with genetic polymorphisms in HLA genes in numerous different studies. HLA proteins have an essential role in the presentation of self and non-self peptides, as part of the adaptive immune response. Amongst the associated drugs-allele combinations, anti-HIV drug Abacavir has been shown to be associated with the HLA-B*57:01 allele, and anti-epilepsy drug Carbamazepine with B*15:02, in both cases likely following the altered peptide repertoire model of interaction. Under this model, the drug binds directly to the antigen presentation region, causing different self peptides to be presented, which trigger an unwanted immune response. There is growing interest in searching for evidence supporting this model for other ADRs using bioinformatics techniques. In this study, in silico docking was used to assess the utility and reliability of well-known docking programs when addressing these challenging HLA-drug situations. The overall aim was to address the uncertainty of docking programs giving different results by completing a detailed comparative study of docking software, grounded in the MHC-ligand experimental structural data – for Abacavir and to a lesser extent Carbamazepine - in order to assess their performance. Four docking programs: SwissDock, ROSIE, AutoDock Vina and AutoDockFR, were used to investigate if each software could accurately dock the Abacavir back into the crystal structure for the protein arising from the known risk allele, and if they were able to distinguish between the HLA-associated and non-HLA-associated (control) alleles. The impact of using homology models on the docking performance and how using different parameters, such as including receptor flexibility, affected the docking performance were also investigated to simulate the approach where a crystal structure for a given HLA allele may be unavailable. The programs that were best able to predict the binding position of Abacavir were then used to recreate the docking seen for Carbamazepine with B*15:02 and controls alleles.

It was found that the programs investigated were sometimes able to correctly predict the binding mode of Abacavir with B*57:01 but not always. Each of the software packages that were assessed could predict the binding of Abacavir and Carbamazepine within the correct sub-pocket and, with the exception of ROSIE, was able to correctly distinguish between risk and control alleles. We found that docking to homology models could produce poorer quality predictions, especially when sequence differences impact the architecture of predicted binding pockets. Caution must therefore be used as inaccurate structures may lead to erroneous docking predictions. Incorporating receptor flexibility was found to negatively affect the docking performance for the examples investigated. Taken together, our findings help characterise the potential but also the limitations of computational prediction of drug-HLA interactions. These docking techniques should therefore always be used with care and alongside other methods of investigation, in order to be able to draw strong conclusions from the given results.

Efficacy From Strange Sources

Reverse translation (RT) refers to perceptions and observations of clinical outcomes, both beneficial and harmful, that can lead to a hypothesis intended to identify a new use of a drug that is different from the original use. I provide a panoramic view of successes in RT from the historic discovery of penicillin to the contemporary development of CAR (chimeric antigen receptor) T therapies. I delineate the core principles of RT that shift discovery from serendipity to a systematic strategy based on target identification and causal biology, pharmacodynamic biomarkers that recapitulate disease pathophysiology, confirmation of target engagement by clinical proof of concept studies, and optimal selection of dose and interval. The article proposes four different categories of RT and successful examples are provided for each category. It concludes with a summary of open questions related to the business case of RT that includes a comparison of the pros, cons, and barriers to future RT development programs.

Adverse drug reactions triggered by the common HLA-B*57:01 variant: a molecular docking study

BACKGROUND

Human leukocyte antigen (HLA) surface proteins are directly involved in idiosyncratic adverse drug reactions. Herein, we present a structure-based analysis of the common HLA-B*57:01 variant known to be responsible for several HLA-linked adverse effects such as the abacavir hypersensitivity syndrome.

METHODS

First, we analyzed three X-ray crystal structures involving the HLA-B*57:01 protein variant, the anti-HIV drug abacavir, and different co-binding peptides present in the antigen-binding cleft. We superimposed the three complexes and showed that abacavir had no significant conformational variation whatever the co-binding peptide. Second, we self-docked abacavir in the HLA-B*57:01 antigen binding cleft with and without peptide using Glide. Third, we docked a small test set of 13 drugs with known ADRs and suspected HLA associations.

RESULTS

In the presence of an endogenous co-binding peptide, we found a significant stabilization (~2 kcal/mol) of the docking scores and identified several modified abacavir-peptide interactions indicating that the peptide does play a role in stabilizing the HLA-abacavir complex. Next, our model was used to dock a test set of 13 drugs at HLA-B*57:01 and measured their predicted binding affinities. Drug-specific interactions were observed at the antigen-binding cleft and we were able to discriminate the compounds with known HLA-B*57:01 liability from inactives.

CONCLUSIONS

Overall, our study highlights the relevance of molecular docking for evaluating and analyzing complex HLA-drug interactions. This is particularly important for virtual drug screening over thousands of HLA variants as other experimental techniques (e.g., in vitro HTS) and computational approaches (e.g., molecular dynamics) are more time consuming and expensive to conduct. As the attention for drugs' HLA liability is on the rise, we believe this work participates in encouraging the use of molecular modeling for reliably studying and predicting HLA-drug interactions. Graphical abstract.