Similarity and consistency assessment of three major online drug-drug interaction resources.

AIMS: The aim of this study was 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 information from the British National Formulary (BNF), Thesaurus and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs in terms of DDI. 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 6970 (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 were 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.

The Influence of Underlying Disease on Rituximab Pharmacokinetics May be Explained by Target-Mediated Drug Disposition.

BACKGROUND AND OBJECTIVES: Rituximab is an anti-CD20 monoclonal antibody approved in several diseases, including chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). The influence of underlying disease on rituximab pharmacokinetics has never been investigated for several cancer and non-cancer diseases simultaneously. This study aimed at assessing this influence using an integrated semi-mechanistic model accounting for target-mediated elimination of rituximab. METHODS: Rituximab concentration-time data from five studies previously published in patients with CLL, DLBCL, FL, RA, and AAV were described using a two-compartment model with irreversible binding of rituximab to its target antigen. Both underlying disease and target antigen measurements were assessed as covariates. RESULTS: Central volume of distribution was [95% confidence interval] 1.7-fold [1.6-1.9] higher in DLBCL than in RA, FL, and CLL, and it was 1.8-fold [1.6-2.1] higher in RA, FL, and CLL than in AAV. First-order elimination rate constants were 1.8-fold [1.7-2.0] and 1.3-fold [1.2-1.5] higher in RA, DLBCL, and FL than in CLL and AAV, respectively. Baseline latent antigen level (L0) was 54-fold [30-94], 20-fold [11-36], and 29-fold [14-64] higher in CLL, DLBCL, and FL, respectively, than in RA and AAV. In lymphoma, L0 increased with baseline total metabolic tumor volume (p = 6.10-7). In CLL, the second-order target-mediated elimination rate constant (kdeg) increased with baseline CD20 count on circulating B cells (CD20cir, p = 0.0081). CONCLUSIONS: Our results show for the first time that rituximab pharmacokinetics is strongly influenced by underlying disease and disease activity. Notably, neoplasms are associated with higher antigen amounts that result in decreased exposure to rituximab compared to inflammatory diseases. Our model might be used to estimate unbound target amounts in upcoming studies.

Pharmacokinetic Variability of Therapeutic Antibodies in Humans: A Comprehensive Review of Population Pharmacokinetic Modeling Publications.

The pharmacokinetics of monoclonal antibodies is highly variable among patients. Several factors of variability, referred to as covariates in pharmacokinetic modeling, are known to influence this variability, such as body size, sex, antigen mass, serum albumin levels, or the presence of anti-drug antibodies. We propose a quantitative overview of the occurrence of assessment and detection of the main covariates associated with monoclonal antibody pharmacokinetics by comprehensively examining all population pharmacokinetic studies of monoclonal antibodies in humans. If some covariates are often assessed and detected (such as body size, antigen mass, or serum albumin levels), others are less frequently assessed but still may be relevant (e.g., anti-drug antibodies or endogenous immunoglobulin G levels). There is still a large heterogeneity in the relevance, availability, measurement, coding, detection, and interpretation of covariates. These issues deserve thorough investigation, which will help to design the optimal dosing strategy for every monoclonal antibody during its entire lifespan.

Modelling of the Time-Varying Pharmacokinetics of Therapeutic Monoclonal Antibodies: A Literature Review.

Therapeutic monoclonal antibodies (mAbs) are increasingly used to treat a variety of conditions. The sources of their interindividual pharmacokinetic (PK) variability have been extensively studied, but few data on their intraindividual PK variability are available. In this article, we reviewed the published population compartmental models used to describe the time-varying PK of mAbs in clinical settings. Of 189 publications, 13 report the use of time-varying parameters and 30 describe the effects of antidrug antibody (ADA) development. Currently published time-varying models mainly describe fast decreases in clearance due to target-mediated elimination or slow decreases in clearance owing to cachexia reduction. Immunogenicity models mostly describe 'on-off' increases of clearance due to a rapid elimination of mAbs-ADA complexes. Some more sophisticated models attempted to decipher the time course of immunogenic response, notably by accounting for the time of onset and progressive increase in ADA production. Currently available time-varying and immunogenicity models are empirical approximations of the complex mAb disposition, but they emphasize the necessity to account for the temporal variations of mAb PK in model building. The clinical implications of the time-varying PK of mAbs are not fully understood, but some publications reported a link between clearance decrease and disease improvement. The future perspectives offered by this knowledge include the possibility to adapt the regimen to the disease and the patients' state, and also to immune status, and to monitor their evolution by monitoring PK variations.

Non-Linear Rituximab Pharmacokinetics and Complex Relationship between Rituximab Concentrations and Anti-Neutrophil Cytoplasmic Antibodies (ANCA) in ANCA-Associated Vasculitis: The RAVE Trial Revisited.

BACKGROUND AND OBJECTIVES: Rituximab is approved in patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and leads to a decrease of ANCA levels. The objectives of this study were to investigate the non-linear pharmacokinetics of rituximab and the relationship between its concentrations and ANCA levels in AAV patients. METHODS: Ninety-two AAV patients from the RAVE (Rituximab in ANCA-Associated Vasculitis) trial were assessed. Both ANCA anti-myeloperoxidase (MPO-ANCA) and anti-proteinase 3 (PR3-ANCA) levels were used as biomarkers. The pharmacokinetics of rituximab were described using a semi-mechanistic two-compartment model that included a latent target antigen turnover and allowed the estimation of specific target-mediated elimination in addition to its non-specific elimination of rituximab. The effect of rituximab on the ANCA level was described using a semi-mechanistic compartment model with a negative feedback (Friberg) model with no transit compartment. A population modeling approach was used. RESULTS: Our pharmacokinetic and pharmacokinetic-pharmacodynamic (PK-PD) models satisfactorily described both concentration-time and concentration-effect relationship data. The mean (inter-individual standard deviation) estimated non-specific clearance was 0.15 L/day (0.30%) and the target-mediated elimination rate constant was 2.4 × 10-5 nmol/day. The elimination half-lives for MPO-ANCA and PR3-ANCA were 24 and 18 days, respectively. CONCLUSIONS: A non-linear target-mediated elimination of rituximab was detected in AAV patients. Our PK-PD model allowed quantification of the association between rituximab concentrations and ANCA levels. This decrease was deep but delayed, and more sustained in patients with MPO-ANCA than in those with PR3-ANCA. Our results suggest that repeating courses of rituximab might improve the clinical response to rituximab.

CD4+ count-dependent concentration-effect relationship of rituximab in rheumatoid arthritis.

AIMS: Rituximab is approved in rheumatoid arthritis (RA). A substantial decrease in CD4+ count was observed in responders after a single cycle of treatment. This study aimed to describe and quantifying the influence of CD4+ count depletion on the concentration-response relationship of rituximab in RA patients. METHODS: In this retrospective monocentric observational study, 52 patients were assessed. Repeated measurements of rituximab concentrations (pharmacokinetics), CD4+ counts (biomarker) and disease activity score in 28 joints (DAS28, clinical response) were made. Rituximab pharmacokinetics was described using a 2-compartment model, and CD4+ cell counts and DAS28 measurements were described using indirect turnover and direct Emax pharmacokinetic-pharmacodynamic models, respectively. Delay between rituximab concentrations and responses was accounted for by including biophase compartments. RESULTS: Elimination half-life of rituximab was 18 days. The pharmacokinetic-pharmacodynamic model showed that DAS28 response to rituximab was partly associated with CD4+ cell depletion. At 6 months, a deeper DAS28 decrease was observed in patients when CD4+ cell count is decreased: median [interquartile range] of DAS28 was 3.7 [2.9-4.4] and 4.5 [3.7-5.3] in patients with and without CD4+ decrease, respectively. CONCLUSIONS: This is the first study to quantify the relationship between rituximab concentrations, CD4+ count and DAS28 in RA patients. This model showed that approximately 75% of patients had CD4+ count decrease, and that the clinical improvement is 2-fold higher in patients with CD4+ cells decrease than in others.