Aim: The effect of the Dutch nationwide adjustment of reduced 6-TGN target values (from 600-1200 pmol/8x108 RBC to 320-630 pmol/8x108 RBC) on toxicity and clinical outcome of thiopurine treatment in patients with inflammatory bowel disease (IBD) has not yet been established. Therefore the authors determined the incidence of toxicity-induced discontinuations and efficacy at both target concentrations. Methods: This retrospective study was performed in IBD patients treated with azathioprine or mercaptopurine. Two groups were defined: the former target (FT) group with target concentrations of 600-1200 pmol/8x10^8 RBC and the adjusted target (AT) group with target concentrations of 320-630 pmol/8x10^8 RBC. Patients were followed for maximum 52 weeks or until discontinuation of thiopurine therapy. Data were collected from the local hospital electronic health software of Rijnstate Hospital. Results: 151 patients were included, 76 in the FT group and 75 in the AT group. At week 52, 100 out of 150 patients (66%) of the total population discontinued thiopurine therapy. Forty-eight of this discontinuations were due toxicity (48%). The estimated cumulative incidence of toxicity was higher in the FT group compared to the AT group (47% and 35% respectively, p=0.25). No loss of efficacy was seen in the AT group. Conclusion: Reduction of the target range may lead to less toxicity induced discontinuations. In addition, this study did not find any indication that the reduction of the target range diminished efficacy.

Tocilizumab for reduction of mortality in severe COVID-19 patients: how should we GRADE it?Vladimir TrkuljaVladimir Trkulja, MD, PhDDepartment of PharmacologyZagreb University School of MedicineŠalata 1110000 Zagreb, Croatiae-mail: vladimir.trkulja@mef.hrNumber of words: 799Number of figures/tables: 1To the Editor,A recent systematic review/meta-analysis 1 of randomized trials (RCTs) of tocilizumab (plus standard of care [SoC] vs. SoC w/wo placebo) in severe COVID-19 patients was a pleasure to read owing to a clear presentation of a thorough approach to data (e.g., sensitivity analyses, accounting for corticosteroid use, need for mechanical ventilation [MV] at baseline). Authors assigned high quality (certainty) GRADE levels to the evidence of efficacy in reduction of mortality overall (10 RCTs) and in patients without MV at baseline (data from 9 RCTs), and reduction of incident MV (10 RCTs). The grading was based on fixed-effect pooling, likely owing to low inconsistency index (I2) and closely similar fixed-effect and random-effects estimates1. It is this point that deserves a few comments. Conceptually, fixed-effect meta-analysis of RCTs in medicine is rarely justified, since the underlying assumption is practically inevitably violated due to variety of elements contributing to clinical heterogeneity2. The authors1 presented a range of differences in trial designs (e.g., one or repeated tocilizumab dose, more or less use of concomitant corticosteroids, differences in proportion of subjects on MV). When variance across trials is low, fixed and random-effects estimates are numerically close/identical, but the conceptual differences remain. Again, conceptually, the random-effects method is a preferred approach2 (regardless of numerical closeness of fixed/random estimates) and the choice (fixed/random) should not be based on the heterogeneity estimates2. At this point, the issue of the choice of the variance (τ2) estimator should be mentioned. A number of estimators have been explored: performance depends on the nature of the outcome, may vary across trial sizes, depends on the differences in size of included trials, and is problematic when the number of studies is lowe.g.,2-5. Variance reflects on the assigned trial weights and measures of uncertainty about the pooled estimate. While no τ2 estimator is ideal 2-5, it has been suggested that the Paule-Mandel (PM) estimator performs better than the common DerSimonian-Laird estimator for binary outcomes3.Another point to consider is the method to calculate confidence intervals (CIs) around the pooled estimate. While not without certain limitations 6, the Hartung-Knapp-Sidik-Jonkman (HKSJ) method has been repeatedly shown (under variety of scenarios) to result in more adequate coverage probability than the standard method4,7. Figure 1A re-creates meta-analysis (data presented by the authors1) on mortality across the 10 RCTs (all subjects) – it is only that it uses PM variance estimator and HKSJ correction: random-effects estimate suggests that the mean of the distribution of the effects is 0.88 (as reported1), but the CIs extend to 1.04, suggesting that it includes also effects that are somewhat above unity. It also provides prediction intervals (wider) - the best illustration of heterogeneity2,8. When viewed from the present standpoint, data indicate a non-trivial level of imprecision and heterogeneity. The authors themselves reported apparent differences (mortality reduction vs. no reduction) between estimates based on RCTs with a high proportion vs. low proportion of patients concomitantly treated with corticosteroids 1(or those generated accounting only for corticosteroid-treated vs. not treated patients, but such data were very scarce1): so, there is apparent inconsistency of the estimates across clinical settings. As re-created in Figure 1B-C, there was a tendency of reduced mortality in trials with a high proportion of patients co-treated with corticosteroids (corticosteroid treatment regimen likely variable), but with quite some imprecision and heterogeneity; and no such tendency with “low corticosteroid use”. Similarly, in patients not on MV at baseline, there was a consistent reduction in mortality risk across trials with a high proportion of steroid co-treated patients, but not in trials with a low proportion of co-treated patients (Figure 1D-E). There was also a consistent reduction of risk of incident MV in trials with a high proportion of corticosteroid co-treated patients (Figure 1F), whereas the estimate in trials with “low steroid use” is burdened with heterogeneity and imprecision (Figure 1G).Considering the above, if one were to assign a GRADE level9 to evidence of benefit of tocilizumab in severe COVID-19 patients based on the 10 RCTs addressed in the published meta-analysis1, then the following seems reasonable: a) considering (indiscriminately) all 10 RCTs (and all patients), certainty about reduced mortality is closer to “low/moderate” then to “high” due to imprecision (CIs 0.75-1.04) and heterogeneity/inconsistency; b) data on the effect of tocilizumab+corticosteroid combination that could be extracted from the 10 RCTs are scarce. Trials with high vs. low concomitant use of corticosteroids could be perceived as a proxy, but this is indirect, suggestive and not conclusive evidence. Therefore, while the effects of tocilizumab on the risk of incident MV and mortality in patients not on MV at baseline in trials with a high proportion of corticosteroid co-treated patients were consistent and reasonably precisely estimated, certainty about the benefit of tocilizumab (on top of corticosteroids; regimen?) in this setting is at best moderate/low.ReferencesVela D, Vela-Gaxha Z, Rexhepi M, Olloni R, Hyseni V, nallbani R. Efficacy and safety of tocilizumab versus standard of care/placebo in patients with COVID-19; a systematic review and meta-analysis of randomized controlled trials. Br J Clin Pharmacol . 2021; doi: 10.1111/bcp.15124.Higgins JPT, Thomson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Statist Soc A . 2009; 172(Pt1):137-159.Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods . 2016;7(1): 55-79.Langan D, Higgins JPT, Jakson D, Bowden J. Veroniki AA, Kontopantelis E, Viechtbauer W, Simmonds M. A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods. 2019; 10(1):83-98.IntHout J, Ioannidis JPA, Borm GF, Goeman JJ. Small studies are more heterogeneous than large ones: a meta-meta-analysis. J Clin Epidemiol . 2015; 68(8):860-869.Jakson D, Law M, Rucker G, Schwarzer G. The Hartung-Knapp modification for random-effects meta-analysis: a useful refinement but are there any residual concerns? Stat Med . 2017; 36(25):3923-3934.IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method.BMC Med Res Methodol . 2014; 14:25 doi:10.1186/1471-2288-14-25.IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open . 2016; 6:e010247 doi: 10.1136/bmjopen-2015-010247Guyatt GH, Oxman, AD, Vist GE, Kurz R, Falck-Ytter Y, Schunemann HJ. GRADE: what is “quality of evidence” and why is it important to clinicians. BMJ . 2008;336(7651):995-998.Balduzzi S, Rucker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health . 2019; 22(4):153-160.Figure 1 . Re-creation of the published meta-analysis1 using data provided in the published figures: the difference is in that the present estimates are generated using the Paule-Mandel variance estimator (Q-profile method for variance estimate confidence intervals) instead of the DerSimonian-Laired method available in the RevMan software used by the authors1, and Hartung Knapp Sidik Jonkman correction for random effects (see text for explanation). Panel A corresponds to published1Figure 1, panels B and C correspond to published1supplemental Figure S4. Published meta-analysis1 does not include figures that would correspond to panels D-G. Panels E and G are reduced to summaries for brevity. Note that in all meta-analyses point-estimates of I2 and τ2 were low, but the upper limits of their confidence intervals were rather high, particularly when only 4 RCTs were included (except in panel F with highly consistent results across trials). “High%” or “low %” steroid use refers to trials (as presented in the published meta-analysis1) in which >50% or <50% of the patients were co-treated with corticosteroids. Meta-analyses were performed using packagemeta 10 in R.MV – mechanical ventilation; RCT – randomized controlled trial; SoC – standard of care

This realist enquiry applying behavioural theory aimed to identify behavioural mechanisms and contexts that facilitate prescribers tapering opioids. We identified relevant opioid tapering interventions and services from a 2018 international systematic review and a 2019 England-wide survey, respectively. Interventions and services were eligible if they provided information about contexts and/or behavioural mechanisms influencing opioid tapering success. A stakeholder group (n=23) generated draft programme theories based around the 14 domains of the theoretical domains framework. We refined these using the trial and service data. From 71 articles and 21 survey responses, 56 and 16 respectively were included, representing primary care, hospital, specialist pain facilities and prison services. We identified six programme theories that included five behavioural mechanisms: prescribers’ knowledge about how to taper; build prescribers’ beliefs about capabilities to initiate tapering discussions and manage psychological consequences of tapering; perceived professional role in tapering; the environmental context enabling referral to specialists; and facilitating positive social influence by aligning patient: prescriber expectations of tapering. No interventions are addressing all six mechanisms supportive of tapering. Work is required to operationalise programme theories according to organisational structures and resources. An example operationalisation is combining tapering guidelines with information about local excess opioid problems and endorsing these with organisational branding. Prescribers being given the skills and confidence to initiate tapering discussions by training them in cognitive-based interventions and incorporating access to psychological and physical support in the patient pathway. Patients being provided with leaflets about the tapering process and informed about the patient pathway.

Aims: To investigate the effects of ABCB1 DNA methylation in donors on individual differences in tacrolimus blood concentrations following liver transplantation. Methods: Twenty-three donor liver samples carrying the CYP3A5*3/*3 genotype were classified into two groups based on the initial tacrolimus concentration/dose (C0/D) ratio following liver transplantation. ABCB1 mRNA levels in liver tissues and HepG2 cells were determined by qRT-PCR. DNA methylation status in liver tissues and HepG2 cells was determined using Illumina 850 methylation chip sequencing technology and pyrosequencing. 5-Aza-2dC was used to reverse methylation in HepG2 cells. Intracellular tacrolimus concentrations were determined by liquid mass spectrometry. Results: Genome-wide methylation sequencing and pyrosequencing analyses showed that the methylation levels of three ABCB1 CpG sites (cg12501229, cg00634941, and cg05496710) were significantly different between groups with different tacrolimus C0/D ratios. ABCB1 mRNA expression in donor livers was found to be positively correlated with tacrolimus C0/D ratio (r = 0.458, P < 0.05). After treatment with 5-Aza-2-Dc, the methylation levels of the ABCB1 CpG sites in HepG2 cells significantly decreased, and this was confirmed by pyrosequencing; there was also a significant increase in ABCB1 transcription, and this most likely induced a decrease in intracellular tacrolimus concentrations. Conclusions: ABCB1 CpG site methylation affects tacrolimus metabolism in humans by regulating ABCB1 expression. Therefore, ABCB1 DNA methylation in donor livers might be an important epigenetic factor that affects tacrolimus blood concentrations following liver transplantation.

DWP16001 is a novel sodium-glucose cotransporter-2 (SGLT2) inhibitor under development for the treatment of type 2 diabetes mellitus. This study was conducted to evaluate the pharmacokinetics (PK), pharmacodynamics (PD), and safety of DWP16001 after single and multiple doses in healthy subjects. A randomized, double-blind, placebo- and active-controlled, single- and multiple-dose study was conducted. Twelve subjects in each dose group received a single dose (0.2, 0.5, 1.0, 2.0, or 5.0 mg) or multiple doses (0.1, 0.3, 0.5, 1.0, or 2.0 mg once daily for 15 consecutive days) of DWP16001, dapagliflozin 10 mg, or placebo, in a ratio of 8:2:2. Serial blood samples and interval urine samples were collected for PK and PD analyses. Safety and tolerability were assessed throughout the study period. A dose-dependent increase in urinary glucose excretion (UGE) was observed after a single dose, and the steady-state UGE was 50–60 g/day after multiple doses in the dose range of 0.3 – 2.0 mg. DWP16001 was rapidly absorbed with the time to peak plasma concentration of 1.0 – 3.0 hours, and eliminated with a mean elimination half-life of 13 - 29 hours. The systemic exposure of DWP16001 increased proportionally with the dose after multiple administrations in the range of 0.1 – 2.0 mg. DWP16001 was well tolerated in all dose groups. DWP16001 caused glucosuria in a dose-dependent manner, and systemic exposure was observed after multiple doses. DWP16001 was well tolerated up to 5.0 mg after a single oral dose and up to 2.0 mg after multiple oral administration

Aim: Edoxaban is a non-vitamin K antagonist oral anticoagulant (NOAC) widely used for long-term stroke prevention in patients with non-valvular atrial fibrillation (NVAF). Adherence to NOACs is unsatisfactory and decreases over time. The aim of this study was to explore appropriated remedial dosing regimens for non-adherent edoxaban-treated NVAF patients through the Monte Carlo simulation. Methods: Six proposed regimens were compared with the recommendations in the European Heart Rhythm Association (EHRA) guide regarding the trough total deviation time considering both edoxaban plasma concentration and inactive factor Xa activity. Monte Carlo simulations were performed using RxODE based on the published population pharmacokinetics/pharmacodynamics model. Results: The proposed remedial strategies were different from EHRA recommendations and were related to delay duration. The missed dose can be taken immediately if the delay time is within 11 h. When the delay period is between 12 and 19 h, a half dose followed by a regular dosing schedule is recommended. When the delay time exceeds 19 h, a full dose followed by a half dose is preferred compared to that recommended in the EHRA guide. Our proposed strategies resulted in a shorter total deviation time than EHRA guide. Conclusion: PK/PD modelling and simulation are effective in developing and evaluating the remedial strategies of edoxaban, which could help maximize its therapeutic effect.

Concern about potential deleterious effects of pharmaceuticals in the environment is growing fast. From wiping out vulture populations in Asia, to feminization of fish, pharmaceuticals have shown to provoke important consequences albeit at very low concentrations. A recent article addresses the environmental impact of metered-dose inhalers for asthma in the United Kingdom due to the greenhouse effects hydrofluorocarbons they contain. Since 2005, it is mandatory for all new drugs in Europe to be assessed for their environmental impact. Crucially, this regulation solely refers to the active pharmaceutical ingredient, not the “whole medicine” or finished medical product. This can sometimes lead to incongruences. For instance, it does not consider the environmental impact of the hydrofluorocarbons contained in MDIs. Another example is Adasuve®, an antipsychotic (loxapine) aimed at the rapid control of agitation in patients suffering from psychotic disorders. The device was developed as a rapid systemic delivery of loxapine by inhalation of a thermally generated aerosol for single use. Apart from the active substance it holds a medical-grade plastic housing and a lithium battery. Therefore, after every single use, a lithium battery waste is produced. We envision that we are on a brink of a new era in pharmacotherapy, in which environmental aspects of drugs are taken into account. In definitive, we agree with Wilkinson & Woodcock. When considering the environmental impact of pharmaceuticals, we need to take into account the whole package.

AIM: To explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in terms of: (1) interacting drug pairs; (2) severity rating; (3) evidence rating and (4) clinical management recommendations. METHODS: We extracted DDI information from the British National Formulary (BNF), Thesaurus, and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs. We annotated clinical management recommendations either manually, where possible, or through application of a machine learning algorithm. RESULTS: The DIRs contained 51,481 (BNF), 38,037 (Thesaurus), and 65,446 (Micromedex) drug pairs involved in DDIs. The number of common DDIs across the three DIRs was 6,970 (13.54% of BNF, 18.32% of Thesaurus, and 10.65% of Micromedex). Micromedex and Thesaurus overall showed higher levels of similarity in their severity ratings, while the BNF agreed more with Micromedex on the critical severity ratings and with Thesaurus on the least significant ones. Evidence rating agreement between BNF and Micromedex was generally poor. Variation in clinical management recommendations was also identified, with some categories (i.e. Monitor and Adjust dose) showing higher levels of agreement compared to others (i.e. Use with caution, Wash-out, Modify administration). CONCLUSIONS: There is considerable variation in the DDIs included in the examined DIRs, together with variability in categorisation of severity and clinical advice given. DDIs labelled as critical are more likely to appear in multiple DIRs. Such variability in information could have deleterious consequences for patient safety, and there is a need for harmonisation and standardisation.

Aim We aimed to explore the effect of pregnancy on bedaquiline pharmacokinetics and describe bedaquiline exposure in the human milk of mothers treated for rifampicin-resistant TB, where there is no human data available. Methods We performed a longitudinal pharmacokinetic study in pregnant women treated for rifampicin-resistant TB to explore the effect of pregnancy on bedaquiline exposure. Pharmacokinetic sampling was performed at four time-points over six hours in the third trimester, and again at approximately six weeks postpartum. We obtained serial human milk samples from breastfeeding mothers, and a single plasma sample taken from breastfed and non-breastfed infants to assess bedaquiline exposure. We used liquid chromatography-tandem mass spectrometry to perform the human milk and plasma bedaquiline assays, and population pharmacokinetic modelling to interpret the bedaquiline concentrations. Results We recruited 13 women, six of whom completed the ante- and post-partum PK sampling. All participants were HIV-positive on antiretroviral therapy. We observed lower ante- and post-partum bedaquiline exposures than reported in non-pregnant controls. Bedaquiline concentrations in human milk were higher than maternal plasma (milk to maternal plasma ratio: 24:1). A single random plasma bedaquiline and M2 concentration was available in four infants (median age: 6.5 weeks): concentrations in the one breastfed infant were similar to maternal plasma concentrations; concentrations in the three non-breastfed infants were detectable but lower than maternal plasma concentrations. Conclusion We report low exposure of bedaquiline in pregnant women treated for rifampicin-resistant TB. Bedaquiline significantly accumulates in human milk; breastfed infants receive mg/kg doses of bedaquiline equivalent to maternal doses.

Fluvoxamine for COVID-19 ICU patients?Vladimir TrkuljaDepartment of PharmacologyZagreb University School of MedicineŠalata 11, Zagreb, Croativladimir.trkulja@mef.hrNumber of words: 800Number of figures/tables: 1To the Editor,I read with interest a recently BJCP-accepted manuscript on the use of fluvoxamine in COVID-19 patients who needed admission to an intensive care unit (ICU) 1. It was instructive to read about the pre-existing clinical experience and about possible mechanisms of presumed benefits of fluvoxamine in COVID-19. However, attention needs to be drawn to the suggested effect of fluvoxamine quantified as a 40% reduction in instantaneous risk of death. The authors report1 on a cohort (n=51) of patients who, upon ICU admission, were treated with oral fluvoxamine added to the standard of care (SoC) (3x100 mg/day over 15 days, then 2x50 mg/day over 7 days), and who were compared to a cohort (n=51) of SoC-only patients. The two cohorts were said to be matched 1. Based on reported data 1, it appears that the patients were matched exactly in respect to gender and COVID-19 vaccination status, and, seemingly, on a rather narrow age-caliper, but the matching method was not reported 1; not reported was also a measure of matching adequacy – standardized difference (d ), a preferred method of balance assessment (adequate if d <0.1) since independent of the sample size 2. Based on the reported data1, for example, the fluvoxamine – SoC d regarding body mass index was -0.30 (-0.31 in women and -0.29 in men); also, d=-0.122 regarding history of diabetes,d= -0.350 regarding history of treated hypertension,d=-0.11 regarding on-admission APACHE score – all suggesting a considerable imbalance between the two cohorts (lower values in the fluvoxamine cohort). The authors provide Kaplan-Meier curves of time-to-death (or ICU discharge) but without the numbers at risk1. Still, data could be read from the graphs and curves reconstructed (Figure 1A):(i) the first marked difference between the treated and controls occurs during the first 7 days of observation – 3 patients died and 3 were censored in the former, and 11 died and 4 were censored the latter cohort (Figure 1A). This difference in deaths (3 vs. 11) did not change over the entire later period since the overall difference in the number of deaths was 9 (30/51 in treated vs. 39/51 in controls). This would indicate a very rapid-onset (and subsequently “lost”) effect of fluvoxamine, which does not seem pharmacologically plausible. The assumed fluvoxamine mechanisms1 are not of the immediate-onset type; with a 3x100 mg/day dosing, elimination half-life is likely to extend well beyond 30 hours, hence steady-state would be achieved only after 7-10 days 3. Combined with the baseline imbalance between groups, this indicates that the initial separation of the two curves – more or less preserved throughout the entire subsequent period - was likely not attributable to fluvoxamine; (ii) after day 21, and particularly after day 28, the numbers at risk were very low, and after day 35 there were no further events (Figure 1A), hence accounting for the entire curve is likely misleading 4; (iii) although the curves do not cross (Figure 1A), they indicate a possibility that hazard ratio varied over time. Hazard ratio as generated in a Cox proportional hazard model (as done by the authors) is an average of values that can change over time5; it is also inherently prone to selection bias and, even in absence of confounding its interpretation is not straightforward5. This holds for randomized and particularly for non-randomized settings 5. Reconstructed data depicted in Figure 1A were used to fit a complementary log-log model for continuous time process taking into account the first 35 days (no events after that point): the method treats time as a continuous but more “coarsely” measured variable, in intervals of identical length (in this case 7-day intervals, i.e., weeks); based on assumption of constant hazard within the interval, the method provides period-specific (for weeks 1-5) hazard ratios 6, which is likely a preferable option 5. Figure 1B depicts estimated probabilities of death and HRs: it is only during week 1 that the hazard appeared lower in treated – a period during which, as elaborated, fluvoxamine most likely had no effect. Finally, authors fitted a multivariable Cox model 1to substantiate the fluvoxamine effect. With a total of 15 independents in a study with 102 subjects, the model was likely overfitted and susceptible to bias arising from over(unnecessary)-adjustments 7. But more importantly, it included adjustment for renal replacement therapy (RRT), which was actually one of the outcomes. Inadequacy of adjustments for post-exposure outcomes as if they were baseline covariates has been extensively elaborated 8 and almost inevitably results in a considerable bias, regardless of whether the respective variable was actually a mediator or a collider 8. Such adjustments require implementation of marginal structural models or some of the g-estimation methods 9.Overall, the reported difference between the two cohorts of patients is more likely bias arising from design and analysis than evidence supporting a causal effect of fluvoxamine.ReferencesČalušić M, Marčec R, Lukša L et al. Safety and efficacy of fluvoxamine in COVID-19 ICU patients: an open label, prospective cohort trial with matched controls. Br J Clin Pharmacol . 2021; doi: 10.1111/bcp.15126.Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci . 2010; 25(1):1-21.Hiemke C, Hartter S. Pharmacokinetics of selective serotonin reuptake inhibitors. Pharmacol Ther . 2000; 85 (1):11-28.Machin D, Cheung YB, Parmar MKB, eds. Survival analysis: a practical approach . 2nd ed. Chichester, West Sussex: John Wiley & Sons Ltd; 2006. p.38.Hernan MA. The hazards of hazard ratios. Epidemiology 2010; 21(1):13-15.Prentice RL, Gloecker LA. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 1978; 34(1):57-67.Schisterman EF, Core SF, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiological studies. Epidemiology 2009; 20(4):488-495.Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14(4):300-306.Hernan MA, Robins JM, eds. Causal inference: What if . 1st ed. CRC Press LLC; 2019.Figure 1 . Summary of re-analysis of survival data published in ref. 1. A . Reconstructed curves of Kaplan-Meier product-limit estimates. Data1 were read using a digitizing software, and were re-analyzed and curves were drawn using JMP 13 software (SAS Institute Inc., Cary, NC). Upward oriented ticks indicate censorings, downward oriented ticks indicate failures. ICU – intensive care unit. B . Estimated probabilities of death during weeks 1 to 5 by treatment (Fluvox – fluvoxamine) and period-specific hazard ratios (HR) with confidence intervals. A complementary log-log model was fitted to reconstituted data using SAS 9.4 for Windows (SAS Inc., Cary, NC).

Aims: Given the complexity of antithrombotic therapy guidelines especially in patients with combined antithrombotic therapy, there is a risk of inappropriate prescribing and medication errors. In order to prevent this, a multidisciplinary antithrombotic stewardship (ASP) is implemented in our hospital. The primary aim of this study is to determine the efficacy of this ASP by assessing the number of patients on combined antithrombotic therapy for whom one or more interventions are needed. Methods: A prospective cohort study in a large teaching hospital is conducted. Hospitalized patients who received combined antithrombotic therapy in which an oral anticoagulant was combined with one (double therapy) or two (triple therapy) platelet aggregation inhibitors were included. The ASP proactively evaluated the appropriateness of this combined antithrombotic therapy. If needed, ASP improved the concerned therapy. Each improvement measurement by ASP was counted as one intervention. Results: A total of 460 patients were included over a period of 12 months. 251 (54.6%) patients required at least one intervention from the ASP. The most common intervention was to define and document a maximum duration of the combined antithrombotic therapy (65.5%) instead of lifetime use of the combination, to discontinue antithrombotic therapy (19.4%) as the proper indication was lacking and to adjust the dosage (8.1%). Conclusion: As intervention was needed in more than half of the patients on combined antithrombotic therapy, it seems essential to implement an ASP that dedicated evaluates antithrombotic therapy to improve and ensure optimal use and medication safety.

Aim Dose banding is a commonly used method of dose individualisation in which all patients with similar characteristics are allocated to the same dosing group. Dose banding results in some patients receiving less intensive treatment with the potential for a reduction in therapeutic benefit (iatrogenic therapeutic failure). This study aims to explore the effects of dose banding on therapeutic success and failure. Methods This was a simulation study conducted using MATLAB. Virtual patients were simulated under a simple pharmacokinetic model with a predefined target steady-state average concentration (c_(ss,ave)). Clearance was correlated with a covariate used for dosing. Dose individualisation was based on: one-dose-fits-all, covariate based dosing, empirical dose banding, dose banding optimised for benefit:risk only and dose banding optimised for both benefit:risk and minimising iatrogenic therapeutic failure. Results The lowest and highest probabilities of target attainment (PrTA) were 46% for one-dose-fits-all and 72% for fully individualised covariate-based dosing. Neither dosing approach would result in iatrogenic therapeutic failure as lower dose intensities do not occur. Empirical dose banding performed better than once-dose-fits-all with 59% PTA but not as good as either optimised method (64-69% PrTA) while carrying a risk of iatrogenic therapeutic failure in 25% of patients. Optimising for benefit:risk (only) improved PrTA but carried a risk of iatrogenic therapeutic failure of up to 10%. Optimising for benefit:risk and minimising iatrogenic therapeutic failure provided the best balance. Conclusion Future application of dose banding needs to consider both the probability of benefit:risk as well the risk of causing iatrogenic therapeutic failure.

COVID-19 has spread globally, affecting almost 160 million individuals. Elderly and pre-existing patients (such as diabetes, heart disease and asthma), seems more susceptible to serious illness with COVID-19. Roflumilast was licensed for usage in the European Union in July 2010 as a phosphodiesterase-4 (PDE4) inhibitor. Roflumilast has been shown to decrease bleomycin-induced lung fibrosis, lung hydroxyproline, right heart thickning in animal prophylactic. The current study reviewed existing data that the PDE-4 inhibitor protects not just renal tissues but also other major organ systems after COVID-19 infection by decreasing immune cell infiltration. These immune-balancing effects of roflumilast were related with a decrease in oxidative and inflammatory burden, caspase-3 suppression, and increased PKA/cAMP levels in renal and other organ tissue.

Aim: nlmixr offers first-order conditional estimation with or without interaction (FOCE or FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin’s population pharmacokinetics with flip-flop characteristics within nlmixr framework and investigated SAEM and FOCEi’s performance with respect to bias, precision, and robustness. Method: Compartmental pharmacokinetic models were fitted. The final model was determined based on the lowest objective function value and visual inspection of goodness-of-fit plots. To examine flip-flop pharmacokinetics, k_a values of a typical concentration-time profile based on the final model were perturbed and changes in the steepness of the terminal elimination phase were evaluated. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100-times and resultant changes evaluated. Results: A one-compartment model with transit compartment for absorption best described the data. At low n, Stirling’s approximation of n! over-approximated plasma concentration unlike the log-gamma function. Flip-flop pharmacokinetics were evident as the steepness of the terminal elimination phase changed with k_a. Mean rRMSE for fixed-effect parameters was 0.932. When initial estimates were perturbed, FOCEi estimates of k_a and food effect on k_a appeared bimodal and were upward biased. Discussion: nlmixr is reliable for NLMEM even if flip-flop is present but caution should be exercised when using Stirling’s approximation for n! in the transit compartment model. SAEM was marginally superior to FOCEi in bias and precision, but SAEM was superior against initial estimate perturbations.

Background: Mycophenolate mofetil (MMF) is the most widely used second-line agent in auto-immune hepatitis (AIH). It is generally titrated up to patient response and continued for at least two years following complete liver enzyme normalization. However, in this maintenance phase individual dose adjustment to reach mycophenolic acid (MPA) exposure with the best benefit-risk probability may avoid adverse outcomes. The aim of the present study was to develop population pharmacokinetic (popPK) models and Maximum A-Posteriori Bayesian estimators (MAP-BEs) to estimate MPA inter-dose area under the curve (AUC0-12h) in AIH patients administered MMF using nonlinear mixed effect modelling. Methods: We analysed 50 MPA PK profiles from 34 different patients, together with some demographic, clinical, and laboratory test data. The median number of samples per profile, immediately preceding and following the morning MMF dose, was 7 [4 – 10]. PopPK modeling was performed using parametric, top-down, nonlinear mixed effect modelling with NONMEM 7.3. MAP-BEs were developed based on the the best popPK model and the best limited sampling strategy (LSS) selected among several. Results: The pharmacokinetic data were best described by a 2-compartment model, Erlang distribution to describe the absorption phase, and a proportional error. The best MAP-BE relied on the LSS at 0.33, 1 and 3 hours after mycophenolate mofetil dose administration and was very accurate (bias=5.6%) and precise (RMSE<20%). Conclusion: The precise and accurate Bayesian estimator developed in this study for AIH patients on MMF can be used to improve the therapeutic management of these patients.

There is a growing interest in the psychiatric properties of the dissociative anesthetic ketamine, as single doses have been shown to have fast-acting mood-enhancing and anxiolytic effects, which persist for up to a week after the main psychoactive symptoms have diminished. Therefore, ketamine poses potential beneficial effects in patients with refractory anxiety disorders, where other conventional anxiolytics have been ineffective. Ketamine is a non-competitive antagonist of the N-methyl-D-aspartate (NMDA) glutamate receptor, which underlies its induction of pain relief and anaesthesia. However, the role of NMDA receptors in anxiety reduction is still relatively unknown. To fill this paucity in the literature, this systematic review assesses the evidence that ketamine significantly reduces refractory anxiety and discusses to what extent this may be mediated by NMDA receptor antagonism. We highlight the temporary nature of the anxiolytic effects and discuss the high discrepancy among the study designs regarding many fundamental factors such as administration routes, complementary treatments, and other treatments.

Introduction: In critically ill patients, Transfusion Related Acute Lung Injury (TRALI) remains the leading cause of transfusion-related fatalities in critical care setting and associated with inflammation and oxidative stress state. Recent research raised the potential efficacy of high dose intravenous ascorbic acid in critically ill patients. Objective: The aim of this trial was to investigate the effect of high dose intravenous ascorbic acid (VC) as a targeted therapy for TRALI in terms of serum proinflammatory (interleukin-8, interleukin-1β, C-reactive protein), anti-inflammatory (interleukin-10), oxidative stress (superoxide dismutase, malondialdehyde) markers, and plasma VC levels. Secondary outcomes were oxygenation (PaO2/FiO2 ratio), vasopressor use, duration of mechanical ventilation, ICU length of stay, 7-days mortality and 28-days mortality. Methods: Eighty critically ill patients with TRALI (n=80) were randomized to receive 2.5gm/6hr intravenous vitamin C for 96 hours (ASTRALI group) or placebo. Patients were followed-up to measure the outcomes initially (T0) and at the end of treatment (T96). Results: When compared to control group, ASTRALI group at T96, showed significantly higher median of interleukin-10 (31.6 ± 25.8 Vs. 17.7 ± 12.0 pg/mL, p<0.0001) levels and superoxide dismutase (12876 ± 4627 U/L Vs. 5895 ± 6632 U/L, p<0.0001) activities, lower median C-reactive protein (76 ± 50 Vs. 89 ± 56 mg/L, p=0.033), interleukin-8 (11.8 ± 7.3, 35.5 ± 19.8 pg/mL, p<0.0001), and malondialdehyde (0.197 ± 0.034 Vs. 0.234 ± 0.074 µM/L, p=0.002) levels. Conclusion: High dose ascorbic acid was associated with significantly reduced oxidative stress, reduced pro-inflammatory markers except IL-1β, elevated anti-inflammatory marker, and elevated plasma VC levels

A document by Emma Magavern. Click on the document to view its contents.