Patient-Level Adverse Event Patterns in a Single-Institution Study of the Multi-Kinase Inhibitor Sorafenib.
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ABSTRACT:
SUBMITTER: Karovic S
PROVIDER: S-EPMC5350995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The purpose of assessing adverse events (AEs) in clinical studies is to evaluate what AE patterns are likely to occur during treatment. In contrast, it is difficult to specify which of these patterns occurs in each patient. To tackle this challenging issue, we constructed a new statistical model including nonnegative matrix factorization by incorporating background knowledge of AE-specific structures such as severity and drug mechanism of action. The model uses a meta-analysis framework for integrating data from multiple clinical studies because insufficient information is derived from a single trial. We demonstrated the proposed method by applying it to real data consisting of three Phase III studies, two mechanisms of action, five anticancer treatments, 3317 patients, 848 AE types, and 99,546 AEs. The extracted typical treatment-specific AE patterns coincided with medical knowledge. We also demonstrated patient-level safety profiles using the data of AEs that were observed by the end of the second cycle.
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Project description:Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application "Identification of Kinase-Specific Signal" ( https://gongj.shinyapps.io/ml4ki ). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective.
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Project description:BackgroundSorafenib is an inhibitor of multiple kinases (e.g., VEGF receptors, PDGFR, FLT3, RET, BRAF, KIT) and is approved by FDA for treatment of two adult cancers. The activity of sorafenib was evaluated against the PPTP's in vitro and in vivo panels.ProceduresSorafenib was evaluated against the PPTP in vitro panel using 96-hr exposure at concentrations ranging from 1.0 nM to 10.0 µM. It was tested against the PPTP in vivo panels at a dose of 60 mg/kg administered by oral gavage daily for 5 days per week, repeated for 6 weeks.ResultsIn vitro sorafenib demonstrated cytotoxic activity, with a median IC(50) value of 4.3 µM. Twenty of 23 cell lines had IC(50) values between 1.0 and 10.0 µM. A single cell line (Kasumi-1) with an activating KIT mutation had an IC(50) value < 1.0 µM (IC(50) = 0.02 µM). In vivo sorafenib induced significant differences in event-free survival (EFS) distribution compared to control in 27 of 36 (75%) of the evaluable solid tumor xenografts and in 1 of 8 (12.5%) of the evaluable ALL xenografts. Sorafenib induced tumor growth inhibition meeting criteria for intermediate activity (EFS T/C) in 15 of 34 (44%) evaluable solid tumor xenografts. No xenografts achieved an objective response.ConclusionsThe primary in vitro activity of sorafenib was noted at concentrations above 1 µM, with the exception of a more sensitive cell line with an activating KIT mutation. The primary in vivo effect for sorafenib was tumor growth inhibition, which was observed across multiple histotypes.
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Project description:Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining.
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Project description:Background and objectiveImmune checkpoint inhibitor (ICI)-associated myocarditis is a fatal immune-related adverse events (irAEs), which is prone to affecting multiple organ systems. Multi-organ irAEs have not been fully studied in ICI-associated myocarditis. Therefore, we aimed to explore the impact of multi-organ irAEs on ICI myocarditis in terms of clinical features, treatment, and prognosis.MethodsThis was a retrospective study. The clinical data of ICI myocarditis patients were collected from 6 hospitals in China. The risk factors and characteristics of pure myocarditis and multi-organ irAEs were analyzed. The overall survival (OS) after myocarditis was analyzed and univariate and multivariate regression analysis were performed.ResultsA total of 46 patients were analyzed in this study. Multi-organ irAEs were common (30/46, 65.2%) and prone to severe heart failure. The severe myocarditis was observed in 32 patients (69.6%). When myocarditis occurred, neutrophil to lymphocyte ratio, C-reactive protein, lactate dehydrogenase, interleukin (IL)-6, IL-10, creatine kinase, MB isoenzyme of creatine kinase, and brain natriuretic peptide increased from baseline, but absolute lymphocyte count decreased. Thymoma (B2/B3) was a risk factor for multi-organ irAEs. Heart failure and myocarditis were more severe in patients with multi-organ irAEs and require early corticosteroid therapy (<24 hours). Univariate analysis showed that age ≥ 60 years, myocarditis (grade 3-4), heart failure (grade 3-4), multi-organ irAEs, and severe myocarditis were associated with OS after myocarditis. After adjusting for other factors, heart failure (grade 3-4) was an independent risk factor for immune-related myocarditis (HR: 6.655, 95% CI: 1.539-28.770, p=0.011).ConclusionPatients with ICI-associated myocarditis had multi-organ irAEs with a high incidence of severe myocarditis, mortality, and poor prognosis. Thymoma was prone to those patients with multiple organs involvement. Patients could benefit from early corticosteroid intervention. Heart failure (grade 3-4) was an independent risk factor for OS after myocarditis.
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Project description:IntroductionA major challenge in cancer medicine is the safe and effective delivery of drugs to the right tissue at the right time. Despite being designed for greater target specificity, many drugs still result in side effects and lack of safety in patients following global dissemination. Therefore, to develop new, more effective formulations capable of improving specificity and reducing off-target effects, here we describe formulation of drug crystals, from even a very hydrophobic and otherwise difficult to solubilize small molecule chemical compound, capable of providing constant drug release for weeks following a single injection.MethodsWe chose to utilize the multi-tyrosine kinase inhibitor and multi-modal (anti-angiogenic and tumor cell cytotoxic) agent sorafenib, to combat aberrant angiogenesis and tumor growth which contribute to metastasis, ultimately responsible for poor patient outcomes. We tuned crystal size (surface area:volume ratios), imaged by SEM, to display controllability of drug delivery kinetics in in vitro drug release assays.ResultsSingle and powder crystal X-ray diffraction (XRD) established that all crystals were the same polymorph and drug form. When utilized against an orthotopic triple negative breast cancer (TNBC) mouse model (4T1 in syngeneic BALB/c mice), we established anti-tumor activity from a single local, subcutaneous injection of crystalline sorafenib.ConclusionFrom our findings, we support that engineering crystalline drug delivery systems has implications in the treatment of cancer or other diseases where high enough constitutive drug levels are needed to maintain target saturation and inhibition while also preventing emergence of drug resistance, which is a consequence often seen with suboptimal dosing.Supplementary informationThe online version contains supplementary material available at 10.1007/s12195-021-00708-6.
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Project description:The lack of an effective medical treatment for adrenocortical carcinoma (ACC) has prompted the search for better treatment protocols for ACC neoplasms. Sorafenib, a tyrosine kinase inhibitor has exhibited effectiveness in the treatment of different human tumors. Therefore, the aim of this study was to understand the mechanism through which sorafenib acts on ACC, especially since treatment with sorafenib alone is sometimes unable to induce a long-lasting antiproliferative effect in this tumor type. The effects of sorafenib were tested on the ACC cell line H295R by evaluating cell viability, apoptosis and VEGF receptor signaling which was assessed by analyzing VE-cadherin and β-catenin complex formation. We also tested sorafenib on an in vitro 3D cell culture model using the same cell line. Apoptosis was observed after sorafenib treatment, and coimmunoprecipitation data suggested that the drug prevents formation VEGFR-VE-cadherin and β-catenin proteins complex. These results were confirmed both by ultrastructural analysis and by a 3D model where we observed a disaggregation of spheres into single cells, which is a crucial event that represents the first step of metastasis. Our findings suggest that although sorafenib induces apoptotic cell death a small portion of cells survive the treatment and have characteristics of a malignancy. Based on our data we recommend against the use of sorafenib in patients with ACC.
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Project description:IntroductionSmall molecule tyrosine kinase inhibitors (TKIs) inhibit not only the target kinase but also various kinases as off-target inhibitors not mentioned in the package insert. However, there are no reports that comprehensively examine the relationship between adverse events and kinase affinity.ObjectiveIn this study, we combined basic data and clinical data to visualize the relationship between kinase affinity and adverse events, which will be useful for the management of adverse events in clinical practice.MethodsWe targeted TKIs that have been used domestically and for which the dissociation constant was obtained as reported by Davis et al. Adverse event data recorded in the Japanese Adverse Drug Event Report (JADER) database provided by the Pharmaceuticals and Medical Devices Agency between April 2004 and January 2018 were used. We calculated the reporting rates of the Standardized MedDRA Queries (SMQ) for the adverse events of interest and visualized the correlation coefficients with kinase affinity. We used the adverse events associated with VEGFR2 and EGFR to assess their validity.ResultsWe found a correlation among known kinase-related adverse events, suggesting that the methodology may be used as a signal detection method to generate hypotheses for clinical and basic research.ConclusionOur comprehensive analysis of the kinase affinity of TKIs in this study, which was based on basic TKI kinase affinity data and the clinical data of the reporting rates, suggested that our comprehensive analysis method is useful for generating hypotheses about possible causal relationships between pharmacological effects and adverse events.
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Project description:Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.
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Project description:Our objective was to develop a model for measuring re-identification risk that more closely mimics the behaviour of an adversary by accounting for repeated attempts at matching and verification of matches, and apply it to evaluate the risk of re-identification for Canada's post-marketing adverse drug event database (ADE).Re-identification is only demonstrably plausible for deaths in ADE. A matching experiment between ADE records and virtual obituaries constructed from Statistics Canada vital statistics was simulated. A new re-identification risk is considered, it assumes that after gathering all the potential matches for a patient record (all records in the obituaries that are potential matches for an ADE record), an adversary tries to verify these potential matches. Two adversary scenarios were considered: (a) a mildly motivated adversary who will stop after one verification attempt, and (b) a highly motivated adversary who will attempt to verify all the potential matches and is only limited by practical or financial considerations.The mean percentage of records in ADE that had a high probability of being re-identified was computed.Under scenario (a), the risk of re-identification from disclosing the province, age at death, gender, and exact date of the report is quite high, but the removal of province brings down the risk significantly. By only generalizing the date of reporting to month and year and including all other variables, the risk is always low. All ADE records have a high risk of re-identification under scenario (b), but the plausibility of that scenario is limited because of the financial and practical deterrent even for highly motivated adversaries.It is possible to disclose Canada's adverse drug event database while ensuring that plausible re-identification risks are acceptably low. Our new re-identification risk model is suitable for such risk assessments.
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