Precision medicine and management of rheumatoid arthritis

Functional capacity vs side effects: treatment attributes to consider when individualising treatment for patients with rheumatoid arthritis

Introduction

Individualisation of rheumatoid arthritis (RA) treatment needs to take account of individual patients’ preferences to increase patient-centeredness in treatment decisions. The aim of this study was to identify patient-relevant treatment attributes to consider when individualising treatment for patients with RA.

Method

Patients with RA in Sweden were invited to rank the most important treatment attributes in an online survey (April to May 2020). Semi-structured interviews were conducted (October to November 2020) to further identify and frame potential attributes for shared decision-making. The interviews were audio-recorded, transcribed and analysed using thematic framework analysis. Patient research partners and rheumatologists supported the selection and framing of the treatment attributes across the assessment.

Results

The highest ranked attributes (N = 184) were improved functional capacity, reduced inflammation, reduced pain and fatigue and the risk of getting a severe side effect. The framework analysis revealed two overarching themes for further exploration: treatment goals and side effects. ‘Treatment goals’ emerged from functional capacity, revealing two dimensions: physical functional capacity and psychosocial functional capacity. ‘Side effects’ revealed that mild and severe side effects were the most important to discuss in shared decision-making.

Conclusions

Functional capacity (physical and psychosocial) and potential side effects (mild and severe) are important treatment attributes to consider when individualising RA treatment. Future research should assess how patients with RA weigh benefits and risks against each other, in order to increase patient-centeredness early on the treatment trajectory.

Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma

(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.

Understanding the Psychological, Physiological, and Genetic Factors Affecting Precision Pain Medicine: A Narrative Review

PURPOSE

Precision pain medicine focuses on employing methods to assess each patient individually, identify their risk profile for disproportionate pain and/or the development of chronic pain, and optimize therapeutic strategies to target specific pathological processes underlying chronic pain. This review aims to provide a concise summary of the current body of knowledge regarding psychological, physiological, and genetic determinants of chronic pain related to precision pain medicine.

METHODS

Following the Scale for the Assessment of Narrative Review Articles (SANRA) criteria, we employed PubMed/Medline to identify relevant articles using primary database search terms to query articles such as: precision medicine, non-modifiable factors, pain, anesthesiology, quantitative sensory testing, genetics, pain medicine, and psychological.

RESULTS

Precision pain medicine provides an opportunity to identify populations at risk, develop personalized treatment strategies, and reduce side effects and cost through elimination of ineffective treatment strategies. As in other complex chronic health conditions, there are two broad categories that contribute to chronic pain risk: modifiable and non-modifiable patient factors. This review focuses on three primary determinants of health, representing both modifiable and non-modifiable factors, that may contribute to a patient's profile for risk of developing pain and most effective management strategies: psychological, physiological, and genetic factors.

CONCLUSION

Consideration of these three domains is already being integrated into patient care in other specialties, but by understanding the role they play in development and maintenance of chronic pain, we can begin to implement both precision and personalized treatment regimens.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study

Introduction

Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.

Methods

The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.

Results

The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.

Conclusion

The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-021-00330-y.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.

Reassessment of Pioglitazone for Alzheimer's Disease

Alzheimer's disease is a quintessential 'unmet medical need', accounting for ∼65% of progressive cognitive impairment among the elderly, and 700,000 deaths in the United States in 2020. In 2019, the cost of caring for Alzheimer's sufferers was $244B, not including the emotional and physical toll on caregivers. In spite of this dismal reality, no treatments are available that reduce the risk of developing AD or that offer prolonged mitiagation of its most devestating symptoms. This review summarizes key aspects of the biology and genetics of Alzheimer's disease, and we describe how pioglitazone improves many of the patholophysiological determinants of AD. We also summarize the results of pre-clinical experiments, longitudinal observational studies, and clinical trials. The results of animal testing suggest that pioglitazone can be corrective as well as protective, and that its efficacy is enhanced in a time- and dose-dependent manner, but the dose-effect relations are not monotonic or sigmoid. Longitudinal cohort studies suggests that it delays the onset of dementia in individuals with pre-existing type 2 diabetes mellitus, which small scale, unblinded pilot studies seem to confirm. However, the results of placebo-controlled, blinded clinical trials have not borne this out, and we discuss possible explanations for these discrepancies.

Effect of total glucosides of paeony and Tripterygium wilfordii polyglycosides on erythrocyte methotrexate polyglutamates in rats, analysed using ultra-high-performance liquid chromatography-tandem mass spectrometry

OBJECTIVES

The aim of the study was to explore the effect of total glucosides of paeony (TGP) and Tripterygium wilfordii polyglycosides (TWP) on erythrocyte methotrexate polyglutamates (MTXPGs), the metabolites of methotrexate (MTX).

METHODS

An ultra-high-performance liquid chromatography (UPLC)-tandem mass spectrometry (MS/MS) method was developed to determine MTXPGs. The effects of MTXPGs were analysed using 24 male Sprague-Dawley rats that were randomly divided into the MTX alone, MTX-TGP combined, and MTX-TWP combined groups. Rats were administered MTX at a dose of 0.9 mg/kg once a week, TGP at 0.054 g/kg and TWP at 1.8 mg/kg three times a day. Venous blood (1.0 ml) was collected at weeks 2, 4, 6, 9, 12 and 15 and then analysed using the developed UPLC-MS/MS method.

KEY FINDINGS

Specificity, linear range, inter-and intra-day precision, recovery, matrix effect and stability of MTXPGs met the standard regulations. This method was successfully used for the detection of MTXPGs. After administration of MTX alone, erythrocyte MTXPGs increased and accumulated in a time- and dose-dependent manner. Compared to MTX alone, the combination with TGP significantly decreased the content of total MTXPGs and short-chain MTXPGs (Methotrexate [MTX/MTXPG1] and 4-amino-10-methylpteroyldiglutamic acid [MTXPG2], P < 0.05), but had no significant effect on long-chain MTXPGs (4-amino-10-methylpteroyltriglutamic acid [MTXPG3], P > 0.05) and very long-chain MTXPGs (4-amino-10-methylpteroyltetraglutamic acid [MTXPG4] and 4-amino-10-methylpteroylpentaglutamic acid [MTXPG5], P > 0.05) at week 15. The combination of MTX with TWP had no significant effect on the content of total MTXPGs, short-chain MTXPGs and long-chain MTXPGs (P > 0.05), but it significantly decreased the content of very long-chain MTXPGs (P < 0.05) at week 15.

CONCLUSIONS

The UPLC-MS/MS method was successfully used to determine MTXPGs in rat erythrocytes. TGP and TWP in combination with MTX affected the production of MTXPGs of different chain lengths in erythrocytes.

Insights Into Leukocyte Trafficking in Inflammatory Arthritis - Imaging the Joint

The inappropriate accumulation and activation of leukocytes is a shared pathological feature of immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Cellular accumulation is therefore an attractive target for therapeutic intervention. However, attempts to modulate leukocyte entry and exit from the joint have proven unsuccessful to date, indicating that gaps in our knowledge remain. Technological advancements are now allowing real-time tracking of leukocyte movement through arthritic joints or in vitro joint constructs. Coupling this technology with improvements in analyzing the cellular composition, location and interactions of leukocytes with neighboring cells has increased our understanding of the temporal dynamics and molecular mechanisms underpinning pathological accumulation of leukocytes in arthritic joints. In this review, we explore our current understanding of the mechanisms leading to inappropriate leukocyte trafficking in inflammatory arthritis, and how these evolve with disease progression. Moreover, we highlight the advances in imaging of human and murine joints, along with multi-cellular ex vivo joint constructs that have led to our current knowledge base.

3D Printing in Development of Nanomedicines

Three-dimensional (3D) printing is gaining numerous advances in manufacturing approaches both at macro- and nanoscales. Three-dimensional printing is being explored for various biomedical applications and fabrication of nanomedicines using additive manufacturing techniques, and shows promising potential in fulfilling the need for patient-centric personalized treatment. Initial reports attributed this to availability of novel natural biomaterials and precisely engineered polymeric materials, which could be fabricated into exclusive 3D printed nanomaterials for various biomedical applications as nanomedicines. Nanomedicine is defined as the application of nanotechnology in designing nanomaterials for different medicinal applications, including diagnosis, treatment, monitoring, prevention, and control of diseases. Nanomedicine is also showing great impact in the design and development of precision medicine. In contrast to the "one-size-fits-all" criterion of the conventional medicine system, personalized or precision medicines consider the differences in various traits, including pharmacokinetics and genetics of different patients, which have shown improved results over conventional treatment. In the last few years, much literature has been published on the application of 3D printing for the fabrication of nanomedicine. This article deals with progress made in the development and design of tailor-made nanomedicine using 3D printing technology.

Therapeutic Efficacy of a Trichinella Spiralis Paramyosin-Derived Peptide Modified With a Membrane-Targeting Signal in Mice With Antigen-Induced Arthritis

Helminth-derived molecules have the ability to modulate the host immune system. Our previous study identified a tetradecapeptide derived from Trichinella spiralis paramyosin (Ts-pmy) that could bind to human complement component C9 to inhibit its polymerization, making the peptide a candidate therapeutic agent for complement-related immune disorders. Here, the peptide underwent an N-terminal modification with a membrane-targeting signal (a unique myristoylated peptide) to improve its therapeutic efficacy. We found that the modified peptide had a binding affinity to human C9 that was similar to that of the original peptide, as confirmed by microscale thermophoresis assays. The binding of the modified peptide to human C9 resulted in the inhibition of C9-related complement activation, as reflected by the decreased Zn²⁺-induced C9 polymerization and the decreased C9-dependent lysis of rabbit erythrocytes. In addition, the original and modified peptides could both bind to recombinant mouse C9 and inhibit the C9-dependent lysis of rabbit erythrocytes in normal mouse serum (NMS), which meant that the peptides could cross the species barrier to inhibit complement activity in mice. Further in vitro and in vivo analyses confirmed that the peptide modification increased the retention time of the peptide. Furthermore, intraarticular injection of the modified peptide markedly ameliorated knee swelling and joint damage in mice with antigen-induced arthritis (AIA), as assessed histologically. These results suggested that the Ts-pmy-derived peptide modified with a membrane-targeting signal was a reasonable candidate therapeutic agent for membrane attack complex (MAC)-related diseases [such as rheumatoid arthritis (RA)] and the study presented a new modification method to improve the potential therapeutic effects of the peptide.

Diagnostic Value of Structural and Functional Neuroimaging in Autoimmune Epilepsy

Epilepsy is a common nervous system disease, which affects about 70 million people all over the world. In 2017, the International League Against Epilepsy (ILAE) considered immune factors as its independent cause, and the concept of autoimmune epilepsy (AE) was widely accepted. Early diagnosis and timely treatment can effectively improve the prognosis of the disease. However, due to the diversity of clinical manifestations, the expensive cost of autoantibody detection, and the increased prevalence in Western China, the difficulty for clinicians in early diagnosis and treatment has increased. Fortunately, convenient and fast imaging examinations are expected to help even more. The imaging manifestations of AE patients were characteristic, especially the combined application of structural and functional neuroimaging, which improved the diagnostic value of imaging. In this paper, several common autoantibodies associated with AE and their structure and function changes in neuroimaging were reviewed to provide help for neurologists to achieve the goal of precision medicine.