Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

Impact of the COVID-19 pandemic on the spontaneous reporting and signal detection of adverse drug events

Abstract External factors severely affecting in a short period of time the spontaneous reporting of adverse events (AEs) can significantly impact drug safety signal detection. Coronavirus disease 2019 (COVID-19) represented an enormous challenge for health systems, with over 767 million cases and massive vaccination campaigns involving over 70% of the worldwide population. This study investigates the potential masking effect on certain AEs caused by the substantial increase in reports solely related to COVID-19 vaccines within various spontaneous reporting systems (SRSs). Three SRSs were used to monitor AEs reporting before and during the pandemic, namely, the World Health Organisation (WHO) global individual case safety reports database (VigiBase®), the United States Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report database (JADER). Findings revealed a sudden over-reporting of 35 AEs (≥ 200%) during the pandemic, with an increment of the RRF value in 2021 of at least double the RRF reported in 2020. This translates into a substantial reduction in signals of disproportionate reporting (SDR) due to the massive inclusion of COVID-19 vaccine reports. To mitigate the masking effect of COVID-19 vaccines in post-marketing SRS analyses, we recommend utilizing COVID-19-corrected versions for a more accurate assessment

The <i>In Vitro</i> Pharmacological Profile of Drugs as a Proxy Indicator of Potential <i>In Vivo</i> Organ Toxicities

The potential of a drug to cause certain organ toxicities is somehow implicitly contained in its full pharmacological profile, provided the drug reaches and accumulates at the various organs where the different interacting proteins in its profile, both targets and off-targets, are expressed. Under this assumption, a computational approach was implemented to obtain a projected anatomical profile of a drug from its <i>in vitro</i> pharmacological profile linked to protein expression data across 47 organs. It was observed that the anatomical profiles obtained when using only the known primary targets of the drugs reflected roughly the intended organ targets. However, when both known and predicted secondary pharmacology was considered, the projected anatomical profiles of the drugs were able to clearly highlight potential organ off-targets. Accordingly, when applied to sets of drugs known to cause cardiotoxicity and hepatotoxicity, the approach is able to identify heart and liver, respectively, as the organs where the proteins in the pharmacological profile of the corresponding drugs are specifically expressed. When applied to a set of drugs linked to a risk of Torsades de Pointes, heart is again the organ clearly standing out from the rest and a potential protein profile hazard is proposed. The approach can be used as a proxy indicator of potential <i>in vivo</i> organ toxicities

iPHACE: integrative navigation in pharmacological space

Summary: The increasing availability of experimentally determined binding affinities for drugs on multiple protein targets requires the design of specific mining and visualization tools that graphically integrate chemical and biological data in an efficient environment. With this aim, we developed iPHACE, an integrative web-based tool to navigate in the pharmacological space defined by small molecule drugs contained in the IUPHAR-DB, with additional interactions present in PDSP. Extending beyond traditional querying and filtering tools, iPHACE offers a means to extract knowledge from the target profile of drugs as well as from the drug profile of protein targets

The polypharmacology of 7 commonly studied antipsychotics in the context of pneumonia, across 34 proteins for which experimentally known pK_i values are available.

<p>The affinities for the receptors TRXA2R and PTAFR concern predicted, not experimentally known, values; these are presented here for comparison purposes. Abbreviations: HTR- serotonin receptors; ADR: adrenergic receptors; DRDs- dopamine receptors; HRHs: histamine receptors; CHRs- muscarinic receptors; KCNH2: hERG transporter, SLC6A3: dopamine transporter, SLC6A4: serotonin transporter; TBXA2R- thromboxane A2 receptor; PTAFR- platelet activating factor receptor. Color coding reflects the experimentally known pK_i values, yellow being inactive (pK_i = 4; K_i = 100 nM), red being highly active (pK_i = 9; K_i = 1 nM), and grey meaning that no data is available for that interaction.</p

Selection of publications in Medline and Web of Science, using the PRISMA model.

<p>Selection of publications in Medline and Web of Science, using the PRISMA model.</p

Clusters identified for investigation for TBXA2R.

<p>TBXA2R is shown in black, associated nodes are represented in blue, primary interactors of TBXA2 are shown in red and secondary interactors are shown in grey. The nodes are linked through physical interactions (data obtained from geneMania database). Abbreviations- ADRB1: Adrenoceptor Beta 1; ARRB2: Arrestin Beta 2; ADRB1: Arrestin Beta 1;AGTR1: Angiotensin II Receptor Type 1; AGTR1: Angiotensin II Receptor Type 1; ADRB2: Adrenoceptor Beta 2; TBXA2R: Thromboxane A2 Receptor; AGTRAP: Angiotensin II Receptor Associated Protein;BDKRB2: Bradykinin Receptor B2;CAV3:Caveolin 3; CDH15: Cadherin 15; CDH2:Cadherin 2;EDNRA: Endothelin Receptor Type A;G3BP2: G3BP Stress Granule Assembly Factor 2; GNA11: G Protein Subunit Alpha 11;GNAI2: G Protein Subunit Alpha I2;GNA12: G Protein Subunit Alpha 12;GNA13: G Protein Subunit Alpha 13;GNAQ: G Protein Subunit Alpha Q;GNAS: GNAS Complex Locus;GPRASP1: G Protein-Coupled Receptor Associated Sorting Protein 1;GRK5: G Protein-Coupled Receptor Kinase 5;HTR2B: 5-Hydroxytryptamine Receptor 2B;HRH2: Histamine Receptor H2; ITGB1BP1: Integrin Subunit Beta 1 Binding Protein 1; KCNMA1: Potassium Calcium-Activated Channel Subfamily M Alpha 1;NME1-MNE2: NME/NM23 nucleoside diphosphate kinase 1;OPRD1:Opioid Receptor Delta 1;OPRK1: Opioid Receptor Kappa 1;OPRM1: Opioid Receptor Mu 1;PRKCA: Protein Kinase C Alpha; RAF1: Raf-1 Proto-Oncogene, Serine/Threonine Kinase;WDR36: WD Repeat Domain 36; YWHAZ: Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Zeta.</p

The polypharmacology of 7 commonly studied antipsychotics in the context of pneumonia, across 34 proteins for which experimentally known pK_i values are available.

<p>The affinities for the receptors TRXA2R and PTAFR concern predicted, not experimentally known, values; these are presented here for comparison purposes. Abbreviations: HTR- serotonin receptors; ADR: adrenergic receptors; DRDs- dopamine receptors; HRHs: histamine receptors; CHRs- muscarinic receptors; KCNH2: hERG transporter, SLC6A3: dopamine transporter, SLC6A4: serotonin transporter; TBXA2R- thromboxane A2 receptor; PTAFR- platelet activating factor receptor. Color coding reflects the experimentally known pK_i values, yellow being inactive (pK_i = 4; K_i = 100 nM), red being highly active (pK_i = 9; K_i = 1 nM), and grey meaning that no data is available for that interaction.</p

Biological processes involved in AP-associated pneumonia related to TBXA2R (upper panel) and PTAFR (lower panel).

<p>Abbreviation- TBXA2R: thromboxane A2 receptor; PTAFR:platelet activating factor receptor.</p

Summary of potential biological mechanisms underlying antipsychotic-associated pneumonia identified in the literature.

<p>Summary of potential biological mechanisms underlying antipsychotic-associated pneumonia identified in the literature.</p