Aims: To continually evaluate the role of cardiovascular drugs in COVID-19 clinical outcomes. Methods: Eligible publications were identified from >500 databases on 1-Nov-2020. One reviewer extracted data with 20% of the records independently extracted/evaluated by a second reviewer. Results: Of 52,735 screened records, 429 and 390 studies were included in the qualitative and quantitative syntheses, respectively. The most-reported drugs were angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) with ACEI/ARB exposure having borderline association with positive COVID-19 status (OR 1.14, 95% CI 1.00–1.31). Among COVID-19 patients, unadjusted estimates showed that ACEI/ARB exposure was associated with hospitalization (OR 1.76, 1.34–2.32), disease severity (OR 1.41, 1.27–1.56) and all-cause mortality (OR 1.22, 1.12–1.33) but not hospitalization length (mean difference -0.27, -1.36; 0.82 days). After adjustment, ACEI/ARB exposure was not associated with positive COVID-19 status (OR 0.92, 0.71–1.19), hospitalization (OR 0.93, 0.70–1.24), disease severity (OR 1.05, 0.81–1.38), or all-cause mortality (OR 0.85, 0.71–1.01). Similarly, subgroup analyses involving only hypertensive patients revealed that ACEI/ARB exposure was not associated with positive COVID-19 status (OR 0.93, 0.79–1.09), hospitalization (OR 0.84, 0.58–1.22), hospitalization length (mean difference -0.14, -1.65; 1.36 days), disease severity (OR 0.92, 0.76–1.11) while it decreased the odds of dying (OR 0.76, 0.65–0.88). A similar trend was observed for other cardiovascular drugs. However, the validity of these findings is limited by a high level of heterogeneity and serious risk of bias. Conclusion: Cardiovascular drugs are not associated with poor COVID-19 outcomes in adjusted analyses. Patients should continue taking these drugs as prescribed.

The hypercoagulable state observed in COVID-19 could be responsible for morbidity and mortality. In this retrospective study we investigated whether therapeutic anticoagulation prior to infection has a beneficial effect in hospitalized COVID-19 patients. 1154 COVID-19 patients admitted to 6 hospitals in the Netherlands between March and May 2020 were included. We applied 1:3 propensity score matching to evaluate the association between prior therapeutic anticoagulation use and clinical outcome, with in hospital mortality as primary endpoint. 190 (16%) patients used therapeutic anticoagulation prior to admission. In the propensity score matched analyses, we observed no associations between prior use of therapeutic anticoagulation and overall mortality (RR 1.02 (95% CI; 0.80-1.30) or length of hospital stay (7.0 [4-12] vs 7.0 {4-12] days, p=0.69), although we observed a lower risk of pulmonary embolism (RR 0.19 (95% CI; 0.05-0.80). This study shows that prior use of therapeutic anticoagulation is not associated with improved clinical outcome in hospitalized COVID-19 patients.

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on three different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (1) cox-proportional hazards modeling and case-matching, (2) tumor growth inhibition-overall survival (TGI-OS) modeling, and (3) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programs. Therefore, the strengths and weaknesses of the other two approaches are considered, and the favorable utility of TGI-OS modeling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials, and proposes that development programs be designed to study more dose levels in earlier trials.

Background Medication harm has negative clinical and economic consequences, contributing to hospitalisation, morbidity and mortality. The incidence ranges from four to 14%, of which up to 50% of events may be preventable. A predictive model for identifying high-risk inpatients can guide a timely and systematic approach to prioritisation. Aim To develop and internally validate a risk prediction model, for prioritisation of hospitalised patients, at risk of medication harm. Methods A retrospective cohort study was conducted in general medical and geriatric specialties at an Australian hospital, over six months. Medication harm was identified using International Classification of Disease (ICD-10) codes and the hospital’s incident database. Sixty-eight variables, including medications and laboratory results, were extracted from the hospital’s databases. Multivariable logistic regression was used to develop the final risk model. Performance was evaluated using area under the receiver operative characteristic curve (AuROC) and clinical utility was determined using decision curve analysis. Results The study cohort included 1982 patients median age 74 years, of which 136 (7%) experienced ≥1 adverse medication event(s). The model included: length of stay, hospital re-admission within 12 months, venous or arterial thrombosis &/or embolism, ≥ 8 medications, serum sodium < 126 mmol/L, INR > 3, anti-psychotic, antiarrhythmic and immunosuppressant medications, and history of medication allergy. Validation gave an AuROC of 0.70 (95% CI: 0.65-0.74). Decision curve analysis identified that the AIME may be clinically useful to help guide decision making in practice. Conclusion We have developed a risk prediction model with reasonable performance. Future steps include external validation.

Objective Polypharmacy is common in people with diabetes and associated with the use of potentially inappropriate medication (PIM). This study aimed to assess trends in prevalence of polypharmacy and PIM in older and middle-aged people with diabetes. Methods A repeated cross-sectional study using the University Groningen IADB.nl prescription database was conducted. All people ≥45 years treated for diabetes registered in the period 2012-2016 were included. PIMs were assessed using Beers criteria for people ≥65 years old, and PRescribing Optimally in Middle-aged People’s Treatments (PROMPT) criteria for 45-64 years old. Chi-square tests and regression analysis were applied. Results The prevalence of polypharmacy increased from 56.5% to 58.2% during the study period. In 2016, the prevalence of polypharmacy was 36.9% in the group of 45-54 years old, 50.3% in 55-64 years old, and 66.2% in ≥65 years old. All age-groups showed significant increases. The prevalence of older people with at least one PIM decreased around 3%, while in the middle-aged group this prevalence increased around 1% with a highest level in 2015. The most common PIMs in both age groups were the use of long-term high-dose proton-pump-inhibitors, benzodiazepines, and strong opioids without laxatives. Of those, only benzodiazepines showed a decreasing trend. Conclusions Polypharmacy increased in older and middle-aged people with diabetes. While the prevalence of PIM decreased over time in older age, this trend was not observed in middle-aged people with diabetes. Efforts are needed to decrease the use of PIMs in populations already burdened with many drugs, notably at middle age

Pregnant, or potentially pregnant women have historically been excluded from clinical trials of new medications. However, it is increasingly recognised that it is imperative to generate evidence from the population in whom the drugs are likely to be used in order to inform safe, evidence-based shared clinical decision making. Reluctance by researchers and regulators to perform such studies often relates to concerns about risk, particularly to the fetus. However, this must be offset against the risk of untreated disease or using a drug in pregnancy where safety, efficacy and dosing information are not known. This review summarises the historical perspective, the ethical and legal frameworks which inform the conduct of such research, then highlights examples of innovative practice which have enabled high quality, ethical research to proceed to inform the evidence-based use of medication in pregnancy.

Diffuse Large B-Cell Lymphoma is the most common type of Non-Hodgkin’s Lymphoma. The disease exhibits significant clinical and biologic heterogeneity. Treatment with standard first line therapy results in cure in about 60% of patients while 30-40%of patients either are refractory to therapy or relapse. Current prognostic scores and biomarkers are unable to accurately predict patients who would relapse or would have refractory disease. A part of the heterogeneity in the behavior of DLBCL is explained by the cell of origin of the tumor. Germinal center type (GCB) DLBCL which is derived from centroblasts are associated with better prognosis compared with activated B-cell type (ABC), which is derived from a B-cell committed to secretory differentiation. While the gold standard for cell of origin determination is gene expression profiling, immunohistochemical methods are routinely used because of more readily available fixed tissue and expertise. Immunohistochemical methods are however associated with a significant degree of discordance with GEP. Within the ABC and GCB types of DLBCL, subgroups of prognostic significance have been identified using various multiple approaches which do not inure themselves to routine practice partly because of limitation of diagnostic material or expertise. Exosomes are a class of membrane bound extracellular vesicles of endosomal origin, produced by multiple cell types. They are involved in intercellular communication and present in abundance in various bodily fluids. Exosomal cargo which includes nucleic acids and proteins can be analyzed, yielding diagnostic and prognostic information in management of DLBCL.

In a recent issue of Br J Clin Pharmacol Smith et al1 published an outstanding commentary titled ‘Dosing will be a key success factor in repurposing antivirals for Covid-19’. They highlighted that the success in our repurposing efforts will be dependent on ‘getting the dose right’ for drugs which have been developed for different indications and stressed some of the unique challenges of treating this particular disease. They pointed the reader to lopinavir/ritonavir (LPV/r) as an example of a repurposed antiviral and the limited experience of this drug regimen (and other treatments) in the elderly population with comorbidities – ie those most at risk from Covid-19. It is on the issue of comorbidities, polypharmacy and drug-drug interactions (DDIs) that we wish to comment.

Aim: We hypothesize that the efficacy of COVID-19 therapeutic candidates will be better predicted by understanding their effects at various points on a viral cell cycle, in particular, the specific rate constants, and that drugs acting independently of these specific discrete sites may not yield expected efficacy. We hypothesize that drugs, or combinations of drugs that act at specific multiple sites on the viral life cycle have the highest probability of success in the treatment of early infection phase in COVID-19 patients. Methods: Using a target cell limited model structure that had been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to show that combinations of therapeutics targeting specific rate constants have greater probability of efficacy and supportive rationale for clinical trial evaluation. Results: Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents. We suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype. Conclusions: Our work highlights the use of model-informed tools to better rationalize effective treatments for COVID-19.

Aims In the absence of a commonly agreed dosing protocol based on pharmacokinetic considerations, the dose and treatment duration for hydroxychloroquine (HCQ) COVID-19 disease currently vary across national guidelines and clinical study protocols. We have used a model-based approach to explore the relative impact of alternative dosing regimens proposed in different dosing protocols for hydroxychloroquine in COVID-19. Methods We compared different PK exposures using Monte Carlo simulations based on a previously published population pharmacokinetic model in patients with rheumatoid arthritis, externally validated using both independent data in lupus erythematous patients and recent data in French COVID-19 patients. Clinical efficacy and safety information from COVID-19 patients treated with HCQ were used to contextualize and assess the actual clinical value of the model predictions. Results Literature and observed clinical data confirm the variability in clinical responses in COVID-19 when treated with the same fixed doses. Confounding factors were identified that should be taken into account for dose recommendation. For 80% of patients, doses higher than 800mg day on D1 followed by 600mg daily on following days might not be needed for being cured. Limited adverse drug reactions have been reported so far for this dosing regimen, most often confounded by co-medications, comorbidities or underlying COVID-19 disease effects. Conclusion Our results were clear indicating the unmet need for characterization of target PK exposures to inform HCQ dosing optimization in COVID-19. Dosing optimization for HCQ in COVID-19 is still an unmet need. Efforts in this sense are a prerequisite for best the benefit/risk balance.

The propellants used in metered-dose inhalers (MDIs) are powerful greenhouse gases, which account for approximately 13% of the NHS’s carbon footprint related to the delivery of care. Most MDI use is in salbutamol relievers in patients with poorly controlled disease. The UK lags behind in this regard with greater reliance on salbutamol MDI and correspondingly greater greenhouse gas emissions; roughly treble our European neighbours’. There has been a broad switch towards MDIs in the UK over the last 20 years to reduce financial costs such that two-thirds of asthma patients in the UK are on treatment dominated by salbutamol MDI. Strategies that replace overuse of reliever MDIs with regimes emphasising inhaled corticosteroids have the potential to improve asthma control alongside significant reductions in greenhouse gas emissions. Real-world evidence shows that once-daily long-acting combination dry-powder inhalers can improve compliance, asthma control and reduce the carbon footprint of care. Similarly, maintenance and reliever therapy (MART) which uses combination reliever and inhaled steroids in one device (usually a dry-powder inhaler) can simplify therapy, improve asthma control and reduce greenhouse gas emissions. Both treatment strategies are popular with patients, most of whom are willing to change treatment to reduce their carbon footprint. By focussing on patients who are currently using high amounts of salbutamol MDI, and prioritising inhaled steroids via dry-powder inhalers, there are golden opportunities to make asthma care more effective, safer and greener.

Aims: Emodepside is an anthelmintic, originally developed for veterinary use. We investigated the safety, pharmacokinetics, relative bioavailability and dose linearity of four oral formulations of emodepside in healthy male subjects. Methods: Three randomised, parallel‐group, controlled, Phase I studies were conducted using various oral formulations, involving 79 subjects in ten cohorts in the single-ascending-dose study, 24 subjects in three ascending-dose cohorts in the multiple-ascending-dose study and 77 subjects in seven different cohorts in the relative bioavailability study. Pharmacokinetics and safety assessments were performed up to 21, 30 and 7 days, respectively. Results: As a liquid service formulation, emodepside was rapidly absorbed under fasting conditions, with dose-proportional increases in plasma concentrations at doses from 1 mg to 40 mg. The half-life during the first 24 hours after dosing was around 11 hours, followed by a terminal elimination half-life > 500 hours. Emodepside was less bioavailable in the fed state. The rate of absorption was slower and Cmax was lower with the amorphous solid dispersion tablets compared to the liquid service formulation. Emodepside was well tolerated overall with no major safety concerns. Conclusion: These Phase I studies with various dosage forms revealed a pharmacokinetic profile suggesting good tissue distribution of emodepside and a long terminal half‐life. A 15 mg dose with the gastrosoluble tablet is predicted to provide exposure that will achieve the target concentration for clinical efficacy. These data enabled us to select a field-adapted tablet formulation that will open the way for further clinical development of emodepside in individuals with onchocerchiasis.

Aim. The multi-part Unified Parkinson’s Disease Rating Scale is the standard instrument in clinical trials. A sum of scores for all items in one or more parts of the instrument is usually analyzed. Without accounting for relative importance of individual items, this sum of scores conceivably does not optimize the power of the instrument. The aim was to compare the ability to detect drug effect in slowing down motor function deterioration, as measured by Part III of the Scale - motor examinations - between the item scores and the sum of scores. Methods. We used data from 423 patients in a Parkinson’s disease progression trial to estimate the symptom severity by item response modelling; modelled symptom progression using the severity and the sum of scores; and conducted simulations to compare the sensitivity of detecting a broad range of hypothetical drug effects on progression using the severity and the sum of scores. Results. The severity endpoint was far more sensitive than the sum of scores for detecting treatment effects, e.g., requiring 280 versus 570 patients per arm to achieve 60% Probability of Success for detecting a range of potential effects in a 2-year trial. Items related to the left side of the body were most informative; and the domain relevance of tremor items was questionable. Conclusion. This analysis generated clear evidence that longitudinal modelling of item scores can enhance trial efficiency and success. It also prompted the needs for a consensus on the placement of the tremor items in the instrument.

Patients in sub-Saharan Africa generally have poor anticoagulation control. We review the potential reasons for this poor control, as well as the potential solutions. Challenges include the affordability and centralisation of anticoagulation care, problems with access to medicines and INR monitoring, the lack of locally-validated standardized dosing protocols, and low levels of anticoagulation knowledge among health care workers and patients. Increasing numbers of patients will need anticoagulation in the future because of the epidemiological transition in the region. We propose that locally-developed “warfarin care bundles” which address multiple anticoagulation challenges in combination may be the most appropriate solution in this setting currently.

The development of specific drug therapy for children was a paradigm changing event that transformed paediatric medical practice. However a series of tragedies involving drug treatment for children resulted in a gap developing between drug regulation and practice, with the majority of drugs used in child health care being used “off label” rendering children therapeutic orphans. Over the past two decades changes in drug regulation led by the US FDA and followed by the European Union’s EMA have led to substantial changes in how new drugs with potential use in children are studied and labelled. While these changes have substantially improved labeling for new drugs, there has been much less progress with older drugs. As well while the unique challenges of conducting clinical research in children have been addressed by novel clinical trial designs, many of these innovations have not been translated into approaches accepted for the drug approval process. The regulations applying to the need for paediatric studies currently are only applicable in the United States and the European Union, and there is less impetus for paediatric labeling in other jurisdictions. This impacts on a number of issues beyond labeling, including the availability of child-friendly formulations. Finally the impact of Brexit on paediatric drug studies in the UK remains unclear and subject to on-going negotiations between the UK government and the European Union.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a global pandemic and urgent treatment and prevention strategies are needed. Nitazoxanide, an anthelmintic drug has been shown to exhibit in vitro activity against SARS-CoV-2. The present study used physiologically-based pharmacokinetic (PBPK) modelling to inform optimal doses of nitazoxanide capable of maintaining plasma and lung tizoxanide exposures above the reported nitazoxanide SARS-CoV-2 EC90. Methods: A whole-body PBPK model was validated against available pharmacokinetic data for healthy individuals receiving single and multiple doses between 500–4000 mg with and without food. The validated model was used to predict doses expected to maintain tizoxanide plasma and lung concentrations above the nitazoxanide EC90 in >90% of the simulated population. PopDes was used to estimate an optimal sparse sampling strategy for future clinical trials. Results: The PBPK model was successfully validated against the reported human pharmacokinetics. The model predicted optimal doses of 1200 mg QID, 1600 mg TID, 2900 mg BID in the fasted state and 700 mg QID, 900 mg TID and 1400 mg BID when given with food. For BID regimens an optimal sparse sampling strategy of 0.25, 1, 3 and 12h post dose was estimated. Conclusion: The PBPK model predicted tizoxanide concentrations within doses of nitazoxanide already given to humans previously. The reported dosing strategies provide a rational basis for design of clinical trials with nitazoxanide for the treatment or prevention of SARS-CoV-2 infection.

Several cases of herpes zoster (HZ) following mRNA COVID-19 vaccination (BNT162b2 and mRNA-1273) have been reported, and first epidemiological evidences suggest an increased risk. We used the worldwide pharmacovigilance database VigiBase to describe HZ cases following mRNA COVID-19 vaccination. We performed disproportionality analyses (case/non-case statistical approach) to assess the relative risk of HZ reporting in mRNA COVID-19 vaccine recipients compared to influenza vaccine recipients and according to patient age. Until 30th June 2021, of 716,928 reports about mRNA COVID-19 vaccines, we found 7,728 HZ cases. When compared to influenza vaccines, mRNA COVID-19 vaccines were associated with a significantly higher reporting of HZ (reporting odds-ratio 1.9, 95%CI [1.8-2.1]). Furthermore, we found a reduced risk of reporting HZ among under 40 year-old persons compared to older persons (reporting odds-ratio 0.39, 95%CI [0.36-0.41]). For the first time, we could assess at a global level the risk of HZ after mRNA COVID-19 vaccination.

Routinely collected data has been increasingly used to evaluate and monitor long-term opioid therapy (LTOT) patterns, with very little guidance on how to measure LTOT from these data sources. We conducted a systematic review of studies published between Jan 2000 and Jul 2019 to catalogue LTOT definitions, the rationale for definitions, and LTOT rates in observational research using routinely collected data in non-surgical settings. We screened 4,056 abstracts, 209 full-text manuscripts and included 126 studies; mostly from the US (82%) and published between 2015 and 2019 (68%). We identified 78 definitions of LTOT, commonly operationalised as 90 days of use within a year (21%). Studies often used multiple criteria to derive definitions (63%), mostly based on measures of duration, such as supply days/days of use (67%), episode length (21%), or prescription fills within specified time periods (9%). Definitions were based on previous publications (63%), clinical judgment (17%), or empirical data (3%); 10% of studies applied more than one definition. LTOT definition was not provided with enough details for replication in 14 studies and 37 studies did not specify the opioids evaluated. Rates of LTOT ranged from 0.2% to 57% according to study design, population and definition used. We observed a substantial rise in studies evaluating LTOT with large variability in the definitions used and poor reporting of the rationale and implementation of the definitions. This variation impacts on research reproducibility, comparability of findings and the development of strategies aiming to curb therapy that is not guideline-recommended.