BMC Infect Dis. 2022 May 18;22(1):476. doi: 10.1186/s12879-022-07411-5.

ABSTRACT

BACKGROUND: Vaccination is a key intervention to prevent COVID-19. Many vaccines are administered globally, yet there is not much evidence regarding their safety and adverse effects. Iran also faces this challenge, especially as data regarding the Sputnik V vaccine is sparse. Therefore, the aim of this study is to determine the adverse effects of the most commonly used vaccines in Iran.

METHODS: Using a retrospective cohort study design, 6600 subjects aged 18 years or older who had received two doses of any of the three COVID-19 vaccines (Sinopharm, AstraZeneca, and Sputnik V) were selected using a random sampling method between March and August 2021. Subjects were asked about any adverse effects of the vaccines by trained interviewers via telephone interview. Vaccine-related adverse effects in individuals during the first 72 h and subsequently following both doses of the vaccines were determined. The demographic variables, type of administered vaccine, adverse effects, and history of the previous infection with COVID-19 were collected. Descriptive statistics (mean, standard deviation) and analytical statistics (Chi-squared and Wilcoxon tests) were performed at a 95% significance level using STATA software version 15 (STATA Corp, College Station, TX, USA).

RESULTS: From 6600 participants, 4775 responded (response rate = 72.3%). Of the participants, 1460 (30.6%) received the AstraZeneca vaccine, 1564 (32.8%) received the Sinopharm vaccine and 1751 (36.7%) received the Sputnik V vaccine. 2653 participants (55.56%) reported adverse effects after the first dose and 1704 (35.7%) after the second dose. Sputnik V caused the most adverse effects with 1449 (82.7%) vaccine recipients reporting symptoms after the first or second dose, compared with 1030 (70.5%) for AstraZeneca and only 585 (37.4%) for the Sinopharm vaccine. The most common adverse effects after the first dose were fatigue (28.37%), chill/fever (26.86%), and skeletal pain (22.38%). These three adverse effects were the same for the second dose, although their prevalence was lower.

CONCLUSIONS: In this study, we demonstrate that the Sputnik V vaccine has the highest rate of adverse effects, followed by the AstraZeneca and Sinopharm vaccines. COVID-19 vaccines used in Iran are safe and there were no reports of serious adverse effects.

PMID:35585518 | DOI:10.1186/s12879-022-07411-5

Adv Exp Med Biol. 2022;1357:415-439. doi: 10.1007/978-3-030-88071-2_17.

ABSTRACT

Nanomaterials (NMs) have important and useful applications in chemical industry, electronics, pharmaceuticals, food and others. Their rapid proliferation presents a dilemma to regulators regarding hazard identification and increased concerns for public health.The Adverse Outcome Pathways (AOPs) are innovative central elements of a toxicological knowledge framework, developed for supporting chemical risk assessment based on mechanistic reasoning. AOPs describe a sequence of causally linked events at different levels of biological organisation, triggered by exposure to a stressor (like chemicals or NMs) leading to an adverse health effect in humans or wildlife. The integrative analysis of the cellular and molecular mechanisms of nanotoxicity towards the identification of connected adverse outcomes drives a sequential line - an AOP landscape definition. Each defined AOP is available for crossing data, linking known and unknown landscapes, reducing the reliance on animal studies, associated costs and ethical issues. NMs have unique properties, with specific associated toxicological challenges, which may represent unknown AOP landscapes.In this chapter, an overview of AOPs as important novel strategic tools in nanotoxicology is presented, highlighting the current applications in hazard identification and human health risk assessment.

PMID:35583654 | DOI:10.1007/978-3-030-88071-2_17

Rev Med Suisse. 2022 May 18;18(782):997-1001. doi: 10.53738/REVMED.2022.18.782.997.

ABSTRACT

In last years, the therapeutic arsenal against breast cancer increased considerably with the arrival of signaling pathway inhibitors, immunotherapy, PARP inhibitors, tyrosine kinase inhibitors and antibody-drug conjugates. Consequently, the range of potential adverse events has also widened and differs from the usual chemotherapies and endocrine therapies. Depending on the administered therapy, the same symptoms can be harmless and treated symptomatically or the warning sign of a potential serious complication requiring a rapid action. We therefore discuss in this article the therapeutic role and some typical adverse events of these new therapies.

PMID:35583279 | DOI:10.53738/REVMED.2022.18.782.997

Drug Ther Bull. 2022 May 18:dtb-2022-000028. doi: 10.1136/dtb.2022.000028. Online ahead of print.

ABSTRACT

Overview of: Dhillon-Smith RK, Middleton LJ, Sunner KK, et al Levothyroxine in women with thyroid peroxidase antibodies before conception. N Engl J Med 2019;380:1316-25.

PMID:35584885 | DOI:10.1136/dtb.2022.000028

Epilepsia. 2022 May 17. doi: 10.1111/epi.17304. Online ahead of print.

ABSTRACT

OBJECTIVE: Evaluate safety/tolerability and efficacy of adjunctive brivaracetam (BRV) in patients on one or two concomitant antiseizure medications (ASMs) and in patients on one specific concomitant ASM.

METHODS: Post hoc analysis of double-blind trials (N01252/NCT00490035, N01253/NCT00464269, and N01358/NCT01261325) in adults with focal seizures randomized to BRV (50-200 mg/day; approved therapeutic dose range for adults) or placebo with concomitant ASM regimen unchanged throughout 12-week evaluation period. Outcomes were analyzed in patients on one or two concomitant ASMs, and those on concomitant carbamazepine (CBZ), lamotrigine (LTG), oxcarbazepine (OXC), or valproate (VPA) only.

RESULTS: Patients randomized to BRV with one or two concomitant ASMs, respectively (n=181/557), reported similar incidences of treatment-emergent adverse events (TEAEs; 68.0%/66.4%), drug-related TEAEs (41.4%/41.5%), and TEAEs leading to discontinuation (6.6%/5.4%). Respective values for patients randomized to placebo with one or two concomitant ASMs (n=95/331) were 60.0%/60.7% (TEAEs), 32.6%/30.2% (drug-related TEAEs), and 2.1%/4.5% (TEAEs leading to discontinuation). The incidences of TEAEs, drug-related TEAEs, and TEAEs leading to discontinuation by specific concomitant ASM (CBZ, LTG, OXC, VPA) were similar to the overall incidences in patients taking one concomitant ASM. In patients on one or two concomitant ASMs, respectively, 50% responder rates were numerically higher on BRV (42.3%/36.8% [n=175/511]) vs placebo (18.3%/19.5% [n=93/298]). Patients with one or two ASMs on BRV (n=175/509) vs placebo (n=92/298) also had numerically higher 100% responder rates (BRV: 9.1%/4.5%; placebo: 1.1%/0.3%) and seizure freedom (6.9%/3.7%; 1.1%/0). For patients taking concomitant CBZ, LTG, OXC, or VPA, efficacy was numerically higher with BRV (n=54/30/27/27) vs placebo (n=34/13/10/14-15; 50% responder rates: BRV, 31.5%/30.0%/40.7%/70.4%; placebo, 17.6%/7.7%/20.0%/33.3%; 100% responder rates: BRV, 5.6%/10.0%/11.1%/11.1%; placebo, 0 for all; seizure freedom: BRV, 3.7%/6.7%/7.4%/11.1%; placebo, 0 for all).

SIGNIFICANCE: Therapeutic doses of BRV were efficacious and well tolerated regardless of the number of concomitant ASMs (one or two) or specific concomitant ASM (CBZ, LTG, OXC, VPA).

PMID:35582748 | DOI:10.1111/epi.17304

BMC Pharmacol Toxicol. 2022 May 17;23(1):30. doi: 10.1186/s40360-022-00570-w.

ABSTRACT

BACKGROUND: Adverse drug events (ADEs) are an important public health problem with considerable clinical and economic costs. However there are limited studies of ADE incidence in adult inpatients in low-income countries, particularly in Ethiopia. Hence, this study aimed to assess the incidence of adverse drug events and associated factors in patients hospitalized in the medical wards of Wolaita Sodo University teaching referral hospital (WSUTRH).

METHODS: A prospective observational study was conducted involving 240 patients admitted to the medical wards of WSUTRH. A checklist was used for data collection, while standard tools were employed for assessing the probability and characterization of ADEs. A multifaceted approach involving daily chart review, patient interview, attendance at ward rounds and/or meetings, and staff reports were employed to collect the data. To identify factors independently associated with ADEs, logistic regression analysis was conducted using Stata version 15.

RESULTS: Patients were followed from ward admission to discharge, accounting for 2200 patient-days of hospital stay. Overall, 976 medications were ordered during the hospital stay. Sixty-four ADEs were identified with an incidence of approximately 27 per 100 admissions and 29 per 1000 patient days. Of the total ADEs, 59% were preventable. Regarding the severity, 2% of the ADEs were severe, while 54% were moderate. The risk of ADEs increased with longer hospital stay (LOHS) (p = 0.021), in patients with blood and immune disease diagnosis (p = 0.001), use of cardiovascular medicines (p = 0.028), and an increase in the number of medications prescribed (p = 0.021).

CONCLUSIONS: In this study, ADEs were identified in about one-quarter of the participants. Longer hospital stays, blood and immune diseases, cardiovascular medicines use, and multiple medication use had increased the likelihood of ADE occurrences. The majority of the ADEs were preventable, indicating the existence of a window of opportunity to ensure patient safety.

PMID:35581618 | PMC:PMC9115930 | DOI:10.1186/s40360-022-00570-w

Drug Saf. 2022 May;45(5):571-582. doi: 10.1007/s40264-022-01163-6. Epub 2022 May 17.

ABSTRACT

INTRODUCTION: Causality assessment of individual case safety reports (ICSRs) is an important step in pharmacovigilance case-level review and aims to establish a position on whether a patient's exposure to a drug is causally related to the patient experiencing an untoward adverse event. There are many different approaches for case causality adjudication, including the use of expert opinions and algorithmic frameworks; however, a great deal of variability exists between assessment methods, products, therapeutic classes, individual physicians, change of process and conventions over time, and other factors.

OBJECTIVE: The objective of this study was to develop a machine learning-based model that can predict the likelihood of a causal association of an observed drug-reaction combination in an ICSR.

METHODS: In this study, we used a set of annotated solicited ICSRs (50K cases) from a company post-marketing database. These data were enriched with novel supplementary features from external and internal data sources that aim to capture facets such as temporal plausibility, scientific validity, and confoundedness that have been shown to contribute to causality adjudication. Using these features, we constructed a Bayesian network (BN) model to predict drug-event pair causality assessment. BN topology was driven by an internally developed ICSR causality decision support tool. Performance of the model was evaluated through examination of sensitivity, positive predictive value (PPV), and the area under the receiver operating characteristic curve (AUC) on an independent set of data from a temporally adjacent interval (20K cases). No external validation was performed because of a lack of publicly available ICSRs with causality assessments for drug-event pairs.

RESULTS: The model demonstrated high performance in predicting the causality assessment of drug-event pairs compared with clinical judgment using global introspection (AUC 0.924; 95% confidence interval [CI] 0.922-0.927). The sensitivity of the model was 0.900 (95% CI 0.896-0.904), and the PPV of the model was 0.778 (95% CI 0.773-0.783).

CONCLUSION: These results show that robust probabilistic modeling of ICSR causality is feasible, and the approach used in the development of the model can serve as a framework for such causality assessments, leading to improvements in safety decision making.

PMID:35579819 | DOI:10.1007/s40264-022-01163-6

Drug Saf. 2022 May;45(5):535-548. doi: 10.1007/s40264-022-01153-8. Epub 2022 May 17.

ABSTRACT

INTRODUCTION: Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports.

OBJECTIVES: We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.

METHODS: Reports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period.

RESULTS: Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.

CONCLUSIONS: Our artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.

PMID:35579816 | PMC:PMC9112264 | DOI:10.1007/s40264-022-01153-8

Drug Saf. 2022 May;45(5):521-533. doi: 10.1007/s40264-022-01160-9. Epub 2022 May 17.

ABSTRACT

INTRODUCTION: Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.

OBJECTIVE: We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.

METHODS: We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.

RESULTS: The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the [Formula: see text] score 0.48) in predicting the drug development outcomes than its un-debiased baseline ([Formula: see text] score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763-1,365 million.

CONCLUSIONS: Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.

PMID:35579815 | DOI:10.1007/s40264-022-01160-9

Drug Saf. 2022 May;45(5):459-476. doi: 10.1007/s40264-022-01155-6. Epub 2022 May 17.

ABSTRACT

Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.

PMID:35579811 | PMC:PMC9114053 | DOI:10.1007/s40264-022-01155-6

Drug Saf. 2022 May;45(5):449-458. doi: 10.1007/s40264-022-01172-5. Epub 2022 May 17.

ABSTRACT

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

PMID:35579810 | PMC:PMC9112241 | DOI:10.1007/s40264-022-01172-5

Drug Saf. 2022 May;45(5):429-438. doi: 10.1007/s40264-022-01157-4. Epub 2022 May 17.

ABSTRACT

There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV.

PMID:35579808 | PMC:PMC9112277 | DOI:10.1007/s40264-022-01157-4

Drug Saf. 2022 May;45(5):407-418. doi: 10.1007/s40264-022-01156-5. Epub 2022 May 17.

ABSTRACT

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.

PMID:35579806 | DOI:10.1007/s40264-022-01156-5

J Med Case Rep. 2022 May 18;16(1):193. doi: 10.1186/s13256-022-03426-3.

ABSTRACT

BACKGROUND: Collecting duct carcinoma and sarcomatoid renal cell carcinoma are tumors with poor prognosis. Immune checkpoint inhibitors have been established as the standard treatment for advanced renal cell carcinoma. Some cases of remission of collecting duct carcinoma and sarcomatoid renal cell carcinoma have been reported using immune checkpoint inhibitor interventions. Specifically, sarcomatoid renal cell carcinoma expresses high levels of programmed death-ligand 1, an immune checkpoint protein, and immune checkpoint inhibitors have been reported to be highly effective for treating sarcomatoid renal cell carcinoma.

CASE PRESENTATION: We describe the case of a 70-year-old Japanese male who underwent radical right nephrectomy for a right renal mass identified on computed tomography. The pathological examination demonstrated that the renal mass was urothelial carcinoma and collecting duct carcinoma with sarcomatoid changes, and programmed death-ligand 1 was highly expressed with a tumor proportion score of more than 10%. There was no evident submucosal connective tissue invasion in the urothelial carcinoma component, and collecting duct carcinoma was diagnosed as primary cancer. The tumor-node-metastasis classification was pT3aN0, venous invasion 1, lymphovascular invasion 0, and Fuhrman nuclear grade 4. Two months after the nephrectomy, multiple metastases were observed in both lungs, the right hilar lymph node, and the S6 segment of the right liver lobe. We initiated first-line combination therapy with nivolumab (240 mg, fixed dose) and ipilimumab (1 mg/kg). One day after administration, the patient developed drug-induced interstitial pneumonia, thus we applied steroid injections. After one administration of immunotherapy, the metastatic lesion showed complete response within 6 months, which was maintained after 3 years.

CONCLUSION: We report the first case of complete response to a single dose of combination therapy with nivolumab and ipilimumab for metastatic collecting duct carcinoma with sarcomatoid changes and high expression of programmed death-ligand 1. This case suggests high expectations for immune checkpoint inhibitors as treatment for sarcomatoid-transformed renal carcinoma tumors that express high levels of programmed death-ligand 1.

PMID:35581611 | DOI:10.1186/s13256-022-03426-3

Drugs Real World Outcomes. 2022 May 17. doi: 10.1007/s40801-022-00298-3. Online ahead of print.

ABSTRACT

OBJECTIVE: Polypharmacy increases the risk of adverse drug events and drug-drug interactions, and contributes to falls, hospital admissions, morbidity and mortality. Veterans with post-traumatic stress disorder often have psychological and physical comorbidities, increasing the likelihood of general and psychotropic polypharmacy. This study investigates the prevalence of general and psychotropic polypharmacy in inpatient veterans with post-traumatic stress disorder, and illustrates potential risks associated with polypharmacy in this population.

METHODS: Medical records of 219 veterans admitted to a mental health facility for post-traumatic stress disorder management were retrospectively reviewed. Medication lists on admission were extracted and coded according to Anatomical Therapeutic Chemical Classification classes. The prevalence of general (five or more total medications), psychotropic (two or more N-code medications), and sedative (two or more medications with sedating effects) polypharmacy and Drug Burden Index were calculated. Class combinations were reported, and associations between demographic characteristics and polypharmacy were determined.

RESULTS: Mean age was 62.5 (± 14.6) years. In addition to post-traumatic stress disorder, 90.9% had a diagnosis of at least one other psychiatric condition, and 96.8% had a diagnosis of at least one non-psychiatric medical condition. The prevalence of general polypharmacy was 76.7%, psychotropic polypharmacy was 79.9% and sedative polypharmacy was 75.3%. Drug Burden Index scores ranged from 0 to 8.2, with 66.2% of participants scoring ≥ 1.

CONCLUSIONS: This cohort of inpatient veterans with post-traumatic stress disorder had a high prevalence of general, psychotropic and sedative polypharmacy, and were at high risk for drug-related adverse events. This highlights the importance of increasing awareness of polypharmacy and potentially inappropriate drug combinations, and the need for improved medication review by prescribers.

PMID:35581527 | DOI:10.1007/s40801-022-00298-3

AIDS Care. 2022 May 16:1-10. doi: 10.1080/09540121.2022.2062289. Online ahead of print.

ABSTRACT

A quantitative descriptive study based on Brazilian Active Pharmacovigilance of Dolutegravir (DTG) Project was performed to describe the adverse drug reactions (ADRs) to DTG reported and to evaluate the noncompleteness of data from DTG active pharmacovigilance in Brazil. ADRs and clinical and individual data were obtained from information from the Pharmacovigilance Questionnaire from April 2017 to August 2019. The reported ADRs were classified using the Medical Dictionary for Regulatory Activities (MedDRA). In the evaluated period, 249,066 individuals using DTG participated in the active pharmacovigilance of DTG, with 3472 (1.39%) reporting ADRs at least once. A total of 6312 ADRs were reported, of which 57.56% were persistent and 81.46% were not serious according to the individuals' reports. Most of the reported ADRs were gastrointestinal, neurological and psychiatric. ADRs related to neural tube defects and serious neuropsychiatric ADRs have been reported. Completion of more than half of the fields in the Pharmacovigilance Questionnaire was excellent. The frequency of ADR was low in relation to the number of people living with HIV (PLHIV) using DTG in Brazil, which suggests good tolerability and safety of DTG. The DTG active pharmacovigilance database in Brazil showed good data completeness.

PMID:35578399 | DOI:10.1080/09540121.2022.2062289

Healthc Inform Res. 2022 Apr;28(2):105-111. doi: 10.4258/hir.2022.28.2.105. Epub 2022 Apr 30.

ABSTRACT

OBJECTIVES: Approximately 20 years have passed since hospital information systems (HISs) featuring full-scale electronic medical records were first implemented in Japan. Patient safety is one of the most important of the several "safety" roles that HISs are expected to fulfill. However, insufficient research has analyzed the contribution of HISs to patient safety. This paper reviews the history of HISs in connection with patient safety in Japan and discusses the future of the patient safety function of HISs in a favorable environment for digitization.

METHODS: A review on the history of HISs with functions that contribute to patient safety was conducted, analyzing evidence from reports published by the Japanese government and papers on patient safety and HISs published in various countries.

RESULTS: Patient safety has become a concern, and initiatives to promote patient safety have progressed simultaneously with the spread of HISs. To address the problem of patient safety, most large hospitals prioritize patients' welfare when building HISs. However, no HIS-associated reduction in adverse events due to medical treatment could be confirmed.

CONCLUSIONS: HISs are expected to help prevent medical accidents, such as patient- and drug-related errors. It is hoped that the patient safety functions of HISs will become generalized and contribute to patient safety in the future. To achieve this, the government and academic societies should provide regulations and guidelines on HISs and patient safety to the medical community and medical-device vendors. Furthermore, departments responsible for HISs and patient safety should collaborate to gather evidence for the effectiveness of HISs.

PMID:35576978 | DOI:10.4258/hir.2022.28.2.105

Blood. 2022 May 16:blood.2022015403. doi: 10.1182/blood.2022015403. Online ahead of print.

ABSTRACT

Polymerization of deoxygenated hemoglobin S (HbS) underlies the pathophysiology of sickle cell disease (SCD). In activating red blood cell pyruvate kinase and glycolysis, mitapivat (AG-348) increases adenosine triphosphate (ATP) levels and decreases the 2,3-diphosphoglycerate (2,3-DPG) concentration, an upstream precursor in glycolysis. Both changes have therapeutic potential for patients with SCD. Here, we evaluated the safety and tolerability of multiple ascending doses of mitapivat in adults with SCD (HbSS) with no recent blood transfusions or changes in hydroxyurea (HU) or L-glutamine therapy. Seventeen subjects were enrolled, 1 subject was withdrawn shortly after starting the study. Sixteen subjects completed 3 ascending dose levels of mitapivat (5 mg, 20 mg and 50 mg, twice daily (BID)) for 2 weeks each; following a protocol amendment, the dose was escalated to 100 mg BID in 9 subjects. Mitapivat was well-tolerated at all dose levels, with the most common treatment-emergent adverse events (AEs) being insomnia, headache, and hypertension. Six serious AEs (SAEs) included 4 vaso-occlusive crises (VOCs), non-VOC-related shoulder pain, and a pre-existing pulmonary embolism. Two VOCs occurred during drug taper and were possibly drug-related; no other SAEs were drug-related. Mean hemoglobin increase at the 50 mg BID dose level was 1.2 g/dL, with 9/16 (56.3%) patients achieving a hemoglobin response of ≥ 1 g/dL increase compared to baseline. Mean reductions in hemolytic markers and dose-dependent decreases in 2,3-DPG and increases in ATP were also observed. This study provides proof of concept that mitapivat has disease-modifying potential in patients with SCD. (Investigator-sponsor; ClinicalTrials.gov NCT04000165).

PMID:35576529 | DOI:10.1182/blood.2022015403

Antimicrob Agents Chemother. 2022 May 16:e0243021. doi: 10.1128/aac.02430-21. Online ahead of print.

ABSTRACT

This study aimed to build a population pharmacokinetic (PopPK) model for contezolid tablet (MRX-I) in healthy subjects and adults with complicated skin and soft-tissue infections (cSSTIs) to further evaluate the efficacy and safety of contezolid and recommend the optimal dosing regimen based on pharmacokinetic/pharmacodynamic (PK/PD) analysis. PopPK analysis was performed using a nonlinear mixed-effects model (NONMEM) to examine the effects of age, body weight, sex, liver and renal functions, albumin, food, dosage strength, and subject type on the PK parameters of contezolid. PK/PD analysis was combined with the MIC of contezolid, clinical/microbiological efficacy, and nonclinical study data. Adverse events (AEs) and study drug-related AEs reported were summarized to examine the relationship between contezolid exposure level and safety measures. A two-compartment model was built. An exponential model was used to describe the interindividual variation. A proportional model was used to describe the intraindividual variation of PK parameters. Good clinical and microbiological efficacy are expected for the infections caused by S. aureus when contezolid is administered at 600 mg or 800 mg every 12 h (q12h). The area under the concentration-time curve from 0 to 24 h at steady state and maximum concentration of drug in serum at steady state of contezolid did not show significant association with the incidence of any AE. The dosing regimen of contezolid at 800 mg q12h administered postprandially for 7 to 14 days is expected to achieve satisfactory clinical and microbiological efficacy in cSSTIs, which is slightly better than that of 600 mg contezolid. This administration has been added to the prescribing information of contezolid tablets.

PMID:35575579 | DOI:10.1128/aac.02430-21

Curr Med Res Opin. 2022 May 14:1-40. doi: 10.1080/03007995.2022.2077579. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate efficacy and tolerability of the nonbenzodiazepine antispasmodic pridinol (PRI), as an add-on treatment in patients with muscle-related pain (MRP).

METHODS: Exploratory retrospective analysis of depersonalized routine data provided by the German Pain e-Registry (GPeR) focusing on pain intensity, pain-related disabilities in daily life, wellbeing, and drug-related adverse events (DRAEs).Primary endpoint based on a global response composite of a) a clinically relevant analgesic response (relative improvement ≥50% and/or absolute improvement ≥ the minimal clinical important difference) for pain intensity and disability in combination with b) an improvement in wellbeing (all at end of treatment vs. baseline), and c) lack of any DRAEs.

RESULTS: Between January 1, 2018, and December 31, 2020, the GPeR collected information on 121,803 pain patients of whom 1,133 (0.9%; 54.5% female, mean ± SD age: 53.9 ± 11.8 years) received add-on PRI for the treatment of (mostly acute) MRP originating predominantly in the (lower) back (43.2%), lower limb (26.4%) or should/neck (21.1%). Average daily dose was 7.8 ± 1.8 (median 9, range 1.5-13.5) mg, duration of treatment 12.0 ± 10.2 (median 7, range 3-63) days. In total, 666 patients (58.8%) reported a complete, 395 (34.9%) a partial and 72 (6.4%) patients no response - either because of lack of efficacy (n = 2, 0.2%) or DRAEs (n = 70, 6.2%). In response to PRI, 41.7% of patients documented a reduction of at least one other pain medication and 30.8% even the complete cessation of any other pharmacological pain treatments.

CONCLUSION: Based on this real-world data of the German Pain e-Registry, add-on treatment with PRI in patients with acute MRP under real-world conditions in daily life was well tolerated and associated with an improvement of pain intensity, pain-related disabilities, and overall wellbeing.

PMID:35575167 | DOI:10.1080/03007995.2022.2077579