Exploring the sequence features determining amyloidosis in human antibody light chains

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.

Safety and Efficacy of Subcutaneous Daratumumab in Systemic AL Amyloidosis

Introduction

Systemic AL amyloidosis, a plasma cell dyscrasia, is characterized by the production of misfolded immunoglobulin light chain. These misfolded proteins aggregate into amyloid fibrils and deposit throughout the body, resulting in widespread organ dysfunction and ultimately death. Achieving rapid and maximal elimination of the plasma cell clone is crucial to long-term survival. Daratumumab, an anti-CD38 monoclonal antibody delivered intravenously, has been swiftly incorporated into standard first-line treatment regimens. A novel formulation of daratumumab has been developed that can be injected subcutaneously.

Areas Covered

As a retrospective qualitative review of prior publications involving daratumumab, this work briefly summarizes the existing data regarding the safety and efficacy of subcutaneous (SC) daratumumab, compared to intravenous (IV) daratumumab. SC daratumumab appears to deliver the same disease benefit as IV daratumumab to patients with decreased infusion-related reactions (IRRs), decreased time for administration, and similar rates of adverse events (AEs) intrinsically related to daratumumab.

Expert Opinion

SC daratumumab is preferred over IV daratumumab, but the clinical situation ultimately should determine route of administration. Further investigation into cost-effectiveness benefit is warranted.

Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe

The adaptive immune system of jawed vertebrates generates a highly diverse repertoire of antibodies to meet the antigenic challenges of a constantly evolving biological ecosystem. Most of the diversity is generated by two mechanisms: V(D)J gene recombination and somatic hypermutation (SHM). SHM introduces changes in the variable domain of antibodies, mostly in the regions that form the paratope, yielding antibodies with higher antigen binding affinity. However, antigen recognition is only possible if the antibody folds into a stable functional conformation. Therefore, a key force determining the survival of B cell clones undergoing somatic hypermutation is the ability of the mutated heavy and light chains to efficiently fold and assemble into a functional antibody. The antibody is the structural context where the selection of the somatic mutations occurs, and where both the heavy and light chains benefit from protective mechanisms that counteract the potentially deleterious impact of the changes. However, in patients with monoclonal gammopathies, the proliferating plasma cell clone may overproduce the light chain, which is then secreted into the bloodstream. This places the light chain out of the protective context provided by the quaternary structure of the antibody, increasing the risk of misfolding and aggregation due to destabilizing somatic mutations. Light chain-derived (AL) amyloidosis, light chain deposition disease (LCDD), Fanconi syndrome, and myeloma (cast) nephropathy are a diverse group of diseases derived from the pathologic aggregation of light chains, in which somatic mutations are recognized to play a role. In this review, we address the mechanisms by which somatic mutations promote the misfolding and pathological aggregation of the light chains, with an emphasis on AL amyloidosis. We also analyze the contribution of the variable domain (V_L) gene segments and somatic mutations on light chain cytotoxicity, organ tropism, and structure of the AL fibrils. Finally, we analyze the most recent advances in the development of computational algorithms to predict the role of somatic mutations in the cardiotoxicity of amyloidogenic light chains and discuss the challenges and perspectives that this approach faces.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

Complete variable domain sequences of monoclonal antibody light chains identified from untargeted RNA sequencing data

Introduction

Monoclonal antibody light chain proteins secreted by clonal plasma cells cause tissue damage due to amyloid deposition and other mechanisms. The unique protein sequence associated with each case contributes to the diversity of clinical features observed in patients. Extensive work has characterized many light chains associated with multiple myeloma, light chain amyloidosis and other disorders, which we have collected in the publicly accessible database, AL-Base. However, light chain sequence diversity makes it difficult to determine the contribution of specific amino acid changes to pathology. Sequences of light chains associated with multiple myeloma provide a useful comparison to study mechanisms of light chain aggregation, but relatively few monoclonal sequences have been determined. Therefore, we sought to identify complete light chain sequences from existing high throughput sequencing data.

Methods

We developed a computational approach using the MiXCR suite of tools to extract complete rearranged IGVL-IGJL sequences from untargeted RNA sequencing data. This method was applied to whole-transcriptome RNA sequencing data from 766 newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass study.

Results

Monoclonal IGVL-IGJL sequences were defined as those where >50% of assigned IGK or IGL reads from each sample mapped to a unique sequence. Clonal light chain sequences were identified in 705/766 samples from the CoMMpass study. Of these, 685 sequences covered the complete IGVL-IGJL region. The identity of the assigned sequences is consistent with their associated clinical data and with partial sequences previously determined from the same cohort of samples. Sequences have been deposited in AL-Base.

Discussion

Our method allows routine identification of clonal antibody sequences from RNA sequencing data collected for gene expression studies. The sequences identified represent, to our knowledge, the largest collection of multiple myeloma-associated light chains reported to date. This work substantially increases the number of monoclonal light chains known to be associated with non-amyloid plasma cell disorders and will facilitate studies of light chain pathology.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

Report of Consensus Panel 6 from the 11 th International Workshop on Waldenström's Macroglobulinemia on Management of Waldenström's Macroglobulinemia Related Amyloidosis

Consensus Panel 6 (CP6) of the 11th International Workshop on Waldenström's Macroglobulinemia (IWWM-11) was tasked with reviewing the state of the art for diagnosis, prognosis, and therapy of AL amyloidosis associated with Waldenström macroglobulinemia (WM). Since significant advances have been made in the management of AL amyloidosis an update for this rare disease associated with WM was necessary. The key recommendations from IWWM-11 CP6 included: (1) The need to improve the diagnostic process by recognizing red flags and using biomarkers and imaging; (2) The essential tests for appropriate workup; (3) The diagnostic flowchart, including mandatory amyloid typing, that improves the differential diagnosis with transthyretin amyloidosis; (4) Criteria for therapy response assessment; (5) State of the art of the treatment including therapy of wild type transthyretin amyloidosis associated with WM.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Understanding AL amyloidosis with a little help from in vivo models

Monoclonal immunoglobulin (Ig) light chain amyloidosis (AL) is a rare but severe disease that may occur when a B or plasma cell clone secretes an excess of free Ig light chains (LCs). Some of these LCs tend to aggregate into organized fibrils with a β-sheet structure, the so-called amyloid fibrils, and deposit into the extracellular compartment of organs, such as the heart or kidneys, causing their dysfunction. Recent findings have confirmed that the core of the amyloid fibrils is constituted by the variable (V) domain of the LCs, but the mechanisms underlying the unfolding and aggregation of this fragment and its deposition are still unclear. Moreover, in addition to the mechanical constraints exerted by the massive accumulation of amyloid fibrils in organs, the direct toxicity of these variable domain LCs, full-length light chains, or primary amyloid precursors (oligomers) seems to play a role in the pathogenesis of the disease. Many in vitro studies have focused on these topics, but the variability of this disease, in which each LC presents unique properties, and the extent and complexity of affected organs make its study in vivo very difficult. Accordingly, several groups have focused on the development of animal models for years, with some encouraging but mostly disappointing results. In this review, we discuss the experimental models that have been used to better understand the unknowns of this pathology with an emphasis on in vivo approaches. We also focus on why reliable AL amyloidosis animal models remain so difficult to obtain and what this tells us about the pathophysiology of the disease.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.