Treatment Horizon in Multiple Myeloma

Autoimmune Diseases and Plasma Cells Dyscrasias: Pathogenetic, Molecular and Prognostic Correlations

Multiple myeloma and monoclonal gammopathy of undetermined significance are plasma cell dyscrasias characterized by monoclonal proliferation of pathological plasma cells with uncontrolled production of immunoglobulins. Autoimmune pathologies are conditions in which T and B lymphocytes develop a tendency to activate towards self-antigens in the absence of exogenous triggers. The aim of our review is to show the possible correlations between the two pathological aspects. Molecular studies have shown how different cytokines that either cause inflammation or control the immune system play a part in the growth of immunotolerance conditions that make it easier for the development of neoplastic malignancies. Uncontrolled immune activation resulting in chronic inflammation is also known to be at the basis of the evolution toward neoplastic pathologies, as well as multiple myeloma. Another point is the impact that myeloma-specific therapies have on the course of concomitant autoimmune diseases. Indeed, cases have been observed of patients suffering from multiple myeloma treated with daratumumab and bortezomib who also benefited from their autoimmune condition or patients under treatment with immunomodulators in which there has been an arising or worsening of autoimmunity conditions. The role of bone marrow transplantation in the course of concomitant autoimmune diseases remains under analysis.

Exploring the role of histone deacetylase and histone deacetylase inhibitors in the context of multiple myeloma: mechanisms, therapeutic implications, and future perspectives

Histone deacetylase inhibitors (HDACis) are a significant category of pharmaceuticals that have developed in the past two decades to treat multiple myeloma. Four drugs in this category have received approval from the U.S. Food and Drug Administration (FDA) for use: Panobinonstat (though canceled by the FDA in 2022), Vorinostat, Belinostat and Romidepsin. The efficacy of this group of drugs is attributed to the disruption of many processes involved in tumor growth through the inhibition of histone deacetylase, and this mode of action leads to significant anti-multiple myeloma (MM) activity. In MM, inhibition of histone deacetylase has many downstream consequences, including suppression of NF-κB signaling and HSP90, upregulation of cell cycle regulators (p21, p53), and downregulation of antiapoptotic proteins including Bcl-2. Furthermore, HDACis have a variety of direct and indirect oxidative effects on cellular DNA. HDAC inhibitors enhance normal immune function, thereby decreasing the proliferation of malignant plasma cells and promoting autophagy. The various biological effects of inhibiting histone deacetylase have a combined or additional impact when used alongside other chemotherapeutic and targeted drugs for multiple myeloma. This helps to decrease resistance to treatment. Combination treatment regimens that include HDACis have become an essential part of the therapy for multiple myeloma. These regimens incorporate drugs from other important classes of anti-myeloma agents, such as immunomodulatory drugs (IMiDs), conventional chemotherapy, monoclonal antibodies, and proteasome inhibitors. This review provides a comprehensive evaluation of the clinical efficacy and safety data pertaining to the currently approved histone deacetylase inhibitors, as well as an explanation of the crucial function of histone deacetylase in multiple myeloma and the characteristics of the different histone deacetylase inhibitors. Moreover, it provides a concise overview of the most recent developments in the use of histone deacetylase inhibitors for treating multiple myeloma, as well as potential future uses in treatment.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Determining Optimal Combination Regimens for Patients with Multiple Myeloma

While many novel therapies have been approved in recent years for treating patients with multiple myeloma, there is still no established curative regimen, especially for patients with high-risk disease. In this work, we use a mathematical modeling approach to determine combination therapy regimens that maximize healthy lifespan for patients with multiple myeloma. We start with a mathematical model for the underlying disease and immune dynamics, which was presented and analyzed previously. We add the effects of three therapies to the model: pomalidomide, dexamethasone, and elotuzumab. We consider multiple approaches to optimizing combinations of these therapies. We find that optimal control combined with approximation outperforms other methods, in that it can quickly produce a combination regimen that is clinically-feasible and near-optimal. Implications of this work can be used to optimize doses and advance the scheduling of drugs.

Bortezomib Pharmacogenetic Biomarkers for the Treatment of Multiple Myeloma: Review and Future Perspectives

Multiple myeloma (MM) is a hematological neoplasm for which different chemotherapy treatments are used with several drugs in combination. One of the most frequently used drugs for the treatment of MM is the proteasome inhibitor bortezomib. Patients treated with bortezomib are at increased risk for thrombocytopenia, neutropenia, gastrointestinal toxicities, peripheral neuropathy, infection, and fatigue. This drug is almost entirely metabolized by cytochrome CYP450 isoenzymes and transported by the efflux pump P-glycoprotein. Genes encoding both enzymes and transporters involved in the bortezomib pharmacokinetic pathway are highly polymorphic. The response to bortezomib and the incidence of adverse drug reactions (ADRs) vary among patients, which could be due to interindividual variations in these possible pharmacogenetic biomarkers. In this review, we compiled all pharmacogenetic information relevant to the treatment of MM with bortezomib. In addition, we discuss possible future perspectives and the analysis of potential pharmacogenetic markers that could influence the incidence of ADR and the toxicity of bortezomib. It would be a milestone in the field of targeted therapy for MM to relate potential biomarkers to the various effects of bortezomib on patients.

Real-world Outcomes With Cumulative Bortezomib Dose and Efficacy in the Treatment of Transplant-ineligible Multiple Myeloma With Cyclophosphamide, Bortezomib, and Dexamethasone

BACKGROUND

Higher cumulative dose of bortezomib, a key component of Multiple Myeloma (MM) treatment regimens, has been shown to improve outcomes in MM patients, but must be balanced with toxicities including peripheral neuropathy. In this study, we studied the effect of cumulative bortezomib dose on survival, depth of response, and discontinuation rate in transplant ineligible MM patients.

PATIENTS AND METHODS

Data from 70 patients treated with Cyclophsophamide, Bortezomib, and Dexamethasone (CyBorD) in a single Canadian center were grouped according to above vs below median cumulative bortezomib dose and analyzed for progression-free survival (PFS), overall survival (OS), depth of response, and discontinuation rate.

RESULTS

There was a trend for lower discontinuation rate (45.7% vs. 68.6%, P = .052) and significantly lower rate of neuropathy-related discontinuation (5.7% vs. 22.9%, P = .035) in patients who received higher than 43.1 mg/m² of bortezomib. The higher-dose group showed a trend for higher rate of complete response (14.3% vs. 5.7%, P = .225) and significantly higher rate of very good partial response or better (77.1% vs. 51.4%, P = .024). There was significantly longer PFS (24.3 vs. 9.1 months, P = .012) and a trend for longer OS (22.4 vs. 61.3 months, P = .061) in the higher-dose group. In landmark analysis after 180 days, PFS (23.5 vs. 24.3 months, P = .941) and OS were similar in both groups.

CONCLUSION

Higher cumulative bortezomib dose showed a lower rate of discontinuation, longer survival, and deeper response. Determining risk of treatment intolerance remains important for treatment.