Mycophenolate mofetil in the treatment of lupus nephritis

Vaginal microbiota affects urinary tract infection risk in women with systemic lupus erythematosus: a pilot cross-sectional study from Thailand

OBJECTIVE

The risk factors associated with urinary tract infections (UTIs) in patients with SLE remain uncertain. We evaluated the vaginal microbiota pattern and its potential UTI-associated risk factors.

METHODS

A pilot cross-sectional study of patients with SLE was conducted at Ramathibodi Hospital, Bangkok, Thailand, during 2019-2020. Patients' demographic data and relevant information were collected. Vaginal microbiota was assessed in all patients and in 10 healthy volunteers.

RESULTS

Fifty-two patients were enrolled (mean age: 46.1 years). All patients had SLE that was in low disease activity. As per the Simpson_e index, the within-group alpha diversity of the vaginal microbiota was low in the SLE with UTI and SLE receiving trimethoprim-sulfamethoxazole (TMP-SMX) prophylaxis groups. Multivariate logistic regression analysis revealed that TMP-SMX prophylaxis (adjusted OR (AOR), 30.96; 95% CI 3.63 to 264.11; p=0.002), elevated C3 levels (AOR, 35.33; 95% CI 1.33 to 936.67; p=0.033) and presence of Veillonella dispar in the vaginal microbiota (AOR, 6.68; 95% CI 1.27 to 35.07; p=0.025) were associated with UTI.

CONCLUSIONS

The vaginal microbiota diversity differed between patients with lupus with and without UTI, and unnecessary administration of TMP-SMX prophylaxis may affect the alpha diversity of the vaginal microbiota.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Clinical Pharmacology and Interplay of Immune Checkpoint Agents: A Yin-Yang Balance

T cells have a central role in immune system balance. When activated, they may lead to autoimmune diseases. When too anergic, they contribute to infection spread and cancer proliferation. Immune checkpoint proteins regulate T cell function, including cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed cell death-1 (PD-1) and its ligand (PD-L1). These nodes of self-tolerance may be exploited pharmacologically to downregulate (CTLA-4 agonists) and activate [CTLA-4 and PD-1/PD-L1 antagonists, also called immune checkpoint inhibitors (ICIs)] the immune system.CTLA-4 agonists are used to treat rheumatologic immune disorders and graft rejection. CTLA-4, PD-1, and PD-L1 antagonists are approved for multiple cancer types and are being investigated for chronic viral infections. Notably, ICIs may be associated with immune-related adverse events (irAEs), which can be highly morbid or fatal. CTLA-4 agonism has been a promising method to reverse such life-threatening irAEs. Herein, we review the clinical pharmacology of these immune checkpoint agents with a focus on their interplay in human diseases.

PAM3 supports the generation of M2-like macrophages from lupus patient monocytes and improves disease outcome in murine lupus

Systematic Lupus Erythematosus (SLE) is an autoimmune syndrome of unclear etiology. While T and B cell abnormalities contribute to disease pathogenesis, recent work suggests that inflammatory M1-like macrophages also play a role. Previous work showed that the TLR2/1 agonist PAM3CSK4 (PAM3) could stimulate normal human monocytes to preferentially differentiate into immunosuppressive M2-like rather than inflammatory M1-like macrophages. This raised the possibility of PAM3 being used to normalize the M1:M2 ratio in SLE. Consistent with that possibility, monocytes from lupus patients differentiated into M2-like macrophages when treated with PAM3 in vitro. Furthermore, lupus-prone NZB x NZW F1 mice responded similarly to weekly PAM3 treatment. Normalization of the M2 macrophage frequency was associated with delayed disease progression, decreased autoantibody and inflammatory cytokine synthesis, reduced proteinuria and prolonged survival in NZB x NZW F1 mice. The ability of PAM3 to bias monocyte differentiation in favor of immunosuppressive macrophages may represent a novel approach to the therapy of SLE.

Drug repurposing from the perspective of pharmaceutical companies

Drug repurposing holds the potential to bring medications with known safety profiles to new patient populations. Numerous examples exist for the identification of new indications for existing molecules, most stemming from serendipitous findings or focused recent efforts specifically limited to the mode of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. A variety of innovative computational methods to enable systematic repurposing screens, experimental as well as through in silico approaches, have emerged. An efficient drug repurposing pipeline requires the combination of access to molecular data, appropriate analytical expertise to enable robust insights, expertise and experimental set-up for validation and clinical development know-how. In this review, we describe some of the main approaches to systematic repurposing and discuss the various players in this field and the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications, as well as the current advantages, considerations and challenges in repurposing as a drug development strategy pursued by pharmaceutical companies.

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

Development and validation of limited sampling strategy equation for mycophenolate mofetil in children with systemic lupus erythematosus

The aim of this study was to establish a limited sample strategy (LSS) to predict the mycophenolic acid (MPA) area under the curve (AUC)_(0-12) in children with systemic lupus erythematosus (SLE). Three months after initiation of mycophenolate mofetil (MMF) 26 children with SLE presented for therapeutic drug monitoring of MPA. On the day of the test, 10 specimens were collected, analyzed, and MPA AUC_(0-12) was calculated. Using step-wise regression analysis, LSS equations were developed. Using bootstrap validation, the predictive performance was calculated. The measured mean (standard deviation) for the trough concentration and AUC_(0-12) were 2.55 (1.57) μg/ml and 62.6 (21.67) mg.h/L, respectively. The range of trough concentrations and AUC_(0-12) were 0.7-5.54 μg/ml and 22.1-104.8 mg.h/L, respectively. The interindividual variability (%CV) for dose normalized AUC_(0-12) and dose normalized C_trough was 46.5% and 61.1%, respectively. The correlation between the concentrations at the different time points and MPA AUC_(0-12) ranged from 0.05 (1.5 h) to 0.56 (4 h). Two LSS equations that included 4 or 5 time points up to 3 h were developed and validated. The 4 point LSS had a correlation (R²) of 0.88 and the 5 point LSS an R² of 0.87. With respect to the 4 point and 5 point MPA LSS AUC_(0-12), the bias was 1.92% and 1.96%, respectively, and the imprecision was 11.24% and 11.28%, respectively. A 4 point LSS which concludes within 3 h after the administration of the MMF dose was developed and validated, to determine the MPA AUC_(0-12) in children with SLE.