Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Amikiya E, Kwame Bukari F. Spatio-temporal characteristics of Tuberculosis in Ghana

Background

Tuberculosis (TB) remains a key contributor to global mortality; ranks second as the most fatal infectious disease and seventh among the top ten causes of death in Ghana. There is insufficient literature regarding the utilization of Bayesian hierarchical models for examining the spatial and spatio-temporal dynamics of tuberculosis risk in Ghana.

Aim

This study addresses this gap by determining TB hotspots regions in Ghana using the Bayesian modeling framework.

Methods

TB data were obtained from the Ghana Health Service and National Tuberculosis Programme for the 10 administrative regions of Ghana, from 2008 to 2017. Spatial and spatio-temporal TB relative risk for each region were estimated under the Bayesian modeling frameworks. Maps for TB risks were created to visualized regions with TB hotspots. Model fitting and parameter estimation were conducted using integrated nested Laplace approximation via R version 4.3.2.

Results

Among the baseline predictors, TB cure rate, TB success rate, knowledge about TB, human immunodeficiency virus (HIV) prevalence, percentage of literacy, and high income were found to be most significant predictors of TB risk in Ghana. We noted an increased risk of TB infection in the Northern zone and the Eastern and Greater Accra regions in the Southern zone. Spatio-temporal distribution of TB infection risk was predominantly concentrated in the Southern zone. Clustering of TB risk was observed among neighboring regions.

Conclusion

Factors influencing tuberculosis (TB) risk in Ghana are TB cure rate, TB success rate, knowledge about TB, HIV prevalence, literacy rate, and income level. The risk distribution was mainly concentrated in the Southern zone, with clusters of TB risk observed among neighboring regions. To achieve a significant reduction in TB cases, it is essential to allocate resources to TB hotspots regions and also implement measures to control significant predictors of TB infection risk.

Navigating the Complex Landscape of Fibrodysplasia Ossificans Progressiva: From Current Paradigms to Therapeutic Frontiers

Fibrodysplasia ossificans progressiva (FOP) is an enigmatic, ultra-rare genetic disorder characterized by progressive heterotopic ossification, wherein soft connective tissues undergo pathological transformation into bone structures. This incapacitating process severely limits patient mobility and poses formidable challenges for therapeutic intervention. Predominantly caused by missense mutations in the ACVR1 gene, this disorder has hitherto defied comprehensive mechanistic understanding and effective treatment paradigms. This write-up offers a comprehensive overview of the contemporary understanding of FOP's complex pathobiology, underscored by advances in molecular genetics and proteomic studies. We delve into targeted therapy, spanning genetic therapeutics, enzymatic and transcriptional modulation, stem cell therapies, and innovative immunotherapies. We also highlight the intricate complexities surrounding clinical trial design for ultra-rare disorders like FOP, addressing fundamental statistical limitations, ethical conundrums, and methodological advancements essential for the success of interventional studies. We advocate for the adoption of a multi-disciplinary approach that converges bench-to-bedside research, clinical expertise, and ethical considerations to tackle the challenges of ultra-rare diseases like FOP and comparable ultra-rare diseases. In essence, this manuscript serves a dual purpose: as a definitive scientific resource for ongoing and future FOP research and a call to action for innovative solutions to address methodological and ethical challenges that impede progress in the broader field of medical research into ultra-rare conditions.

Development of a Predictive Statistical Pharmacological Model for Local Anesthetic Agent Effects with Bayesian Hierarchical Model Parameter Estimation

As an alternative to animal use, computer simulations are useful for predicting pharmacokinetics and cardiovascular activities. For this purpose, we constructed a statistical model to simulate the effects of local anesthetic agents. To train the model, animal experiments were performed on 6-week-old male Hartley guinea pigs. Firstly, the guinea pigs' backs were shaved, then local anesthetic agents were subcutaneously injected, with subsequent stimulation of the anesthetized site with a needle six times at regular intervals. The number of reactions (score value) was counted. In this statistical model, the probability of reacting to needle stimulation was calculated using the elapsed time, type of local anesthetic agent, and presence or absence of adrenaline. Score values were assumed to follow a binomial distribution at the calculated probability. Parameters were estimated using the Bayesian hierarchical model and Hamiltonian Monte Carlo method. The predicted curves using the estimated parameters fitted well the observed animal values. When score values were predicted using randomly generated parameters, the median of duration was similar between animal experiments and simulations (Procaine: 55 min vs. 50 min, Lidocaine: both 60 min, and Mepivacaine: both 85 min). This approach effectively modeled the effects of local anesthetic agents. It is possible to create the simulator using the parameter values estimated in this study.

Responsiveness of the Scale for the Assessment and Rating of Ataxia and Natural History in 884 Recessive and Early Onset Ataxia Patients

OBJECTIVE

The Scale for the Assessment and Rating of Ataxia (SARA) is the most widely applied clinical outcome assessment (COA) for genetic ataxias, but presents metrological and regulatory challenges. To facilitate trial planning, we characterize its responsiveness (including subitem-level relations to ataxia severity and patient-focused outcomes) across a large number of ataxias, and provide first natural history data for several of them.

METHODS

Subitem-level correlation and distribution-based analysis of 1,637 SARA assessments in 884 patients with autosomal recessive/early onset ataxia (370 with 2-8 longitudinal assessments) were complemented by linear mixed effects modeling to estimate progression and sample sizes.

RESULTS

Although SARA subitem responsiveness varied between ataxia severities, gait/stance showed a robust granular linear scaling across the broadest range (SARA < 25). Responsiveness was diminished by incomplete subscale use at intermediate or upper levels, nontransitions ("static periods"), and fluctuating decreases/increases. All subitems except nose-finger showed moderate-to-strong correlations to activities of daily living, indicating that metric properties-not content validity-limit SARA responsiveness. SARA captured mild-to-moderate progression in many genotypes (eg, SYNE1-ataxia: 0.55 points/yr, ataxia with oculomotor apraxia type 2: 1.14 points/yr, POLG-ataxia: 1.56 points/yr), but no change in others (autosomal recessive spastic ataxia of Charlevoix-Saguenay, COQ8A-ataxia). Whereas sensitivity to change was optimal in mild ataxia (SARA < 10), it substantially deteriorated in advanced ataxia (SARA > 25; 2.7-fold sample size). Use of a novel rank-optimized SARA without subitems finger-chase and nose-finger reduces sample sizes by 20 to 25%.

INTERPRETATION

This study comprehensively characterizes COA properties and annualized changes of the SARA across and within a large number of ataxias. It suggests specific approaches for optimizing its responsiveness that might facilitate regulatory qualification and trial design. ANN NEUROL 2023;94:470-485.

Respiratory features of centronuclear myopathy in the Netherlands

Centronuclear myopathy (CNM) is a heterogeneous group of muscle disorders primarily characterized by muscle weakness and variable degrees of respiratory dysfunction caused by mutations in MTM1, DNM2, RYR1, TTN and BIN1. X-linked myotubular myopathy has been the focus of recent natural history studies and clinical trials. Data on respiratory function for other genotypes is limited. To better understand the respiratory properties of the CNM spectrum, we performed a retrospective study in a non-selective Dutch CNM cohort. Respiratory dysfunction was defined as an FVC below 70% of predicted and/or a daytime pCO2 higher than 6 kPa. We collected results of other pulmonary function values (FEV1/FVC ratio) and treatment data from the home mechanical ventilation centres. Sixty-one CNM patients were included. Symptoms of respiratory weakness were reported by 15/47 (32%) patients. Thirty-three individuals (54%) with different genotypes except autosomal dominant (AD)-BIN1-related CNM showed respiratory dysfunction. Spirometry showed decreased FVC, FEV1 & PEF values in all but two patients. Sixteen patients were using HMV (26%), thirteen of them only during night-time. In conclusion, this study provides insight into the prevalence of respiratory symptoms in four genetic forms of CNM in the Netherlands and offers the basis for future natural history studies.

Development of tailored splice-switching oligonucleotides for progressive brain disorders in Europe: development, regulation, and implementation considerations

Splice-modulating antisense oligonucleotides (ASOs) offer treatment options for rare neurological diseases, including those with very rare mutations, where patient-specific, individualized ASOs have to be developed. Inspired by the development of milasen, the 1 Mutation 1 Medicine (1M1M) and Dutch Center for RNA Therapeutics (DCRT) aim to develop patient-specific ASOs and treat eligible patients within Europe and the Netherlands, respectively. Treatment will be provided under a named patient setting. Our initiatives benefited from regulatory advice from the European Medicines Agency (EMA) with regard to preclinical proof-of-concept studies, safety studies, compounding and measuring benefit and safety in treated patients. We here outline the most important considerations from these interactions and how we implemented this advice into our plan to develop and treat eligible patients within Europe.

Progression of Clinical and Eye Movement Markers in Preataxic Carriers of Machado-Joseph Disease

BACKGROUND

Little is known about preclinical stages of Machado-Joseph disease, a polyglutamine disorder characterized by progressive adult-onset ataxia.

OBJECTIVE

We aimed to describe the longitudinal progression of clinical and oculomotor variables in the preataxic phase of disease.

METHODS

Carriers and noncarriers were assessed at three visits. Preataxic carriers (Scale for Assessment and Rating of Ataxia score < 3) expected to start ataxia in ≤4 years were considered near onset (PAN). Progressions of ataxic and preataxic carriers, considering status at the end of the study, were described according to the start (or its prediction) of gait ataxia (TimeToAfterOnset) and according to the study time.

RESULTS

A total of 35 ataxics, 38 preataxics, and 22 noncarriers were included. The "TimeToAfterOnset" timeline showed that Neurological Examination Scale for Spinocerebellar Ataxias (NESSCA; effect size, 0.09), Inventory of Non-Ataxia Symptoms (INAS0.07), and the vestibulo-ocular reflex gain (0.12) progressed in preataxic carriers, and that most slopes accelerate in PAN, turning similar to those of ataxics. In the study time, NESSCA (1.36) and vertical pursuit gain (1.17) significantly worsened in PAN, and 6 of 11 PANs converted to ataxia. For a clinical trial with 80% power and 2-year duration, 57 PANs are needed in each study arm to detect a 50% reduction in the conversion rate.

CONCLUSIONS

NESSCA, INAS, vestibulo-ocular reflex, and vertical pursuit gains significantly worsened in the preataxic phase. The "TimeToAfterOnset" timeline unveiled that slopes of most variables are small in preataxics but increase and reach the ataxic slopes from 4 years before the onset of ataxia. For future trials in preataxic carriers, we recommend recruiting PANs and using the conversion rate as the primary outcome. © 2022 International Parkinson and Movement Disorder Society.

Bayesian Complex Innovative Trial Designs (CIDs) and Their Use in Drug Development for Rare Disease

As the temporal, financial, and ethical cost of randomized clinical trials (RCTs) continues to rise, researchers and regulators in drug discovery and development face increasing pressure to make better use of existing data sources. This pressure is especially high in rare disease, where traditionally designed RCTs are often infeasible due to the inability to recruit enough patients or the unwillingness of patients or trial leaders to randomly assign anyone to placebo. Bayesian statistical methods have recently been recommended in such settings for their ability to combine disparate data sources, increasing overall study power. The use of these methods has received a boost in the United States thanks to a new willingness by regulators at the Food and Drug Administration to consider complex innovative trial designs. These designs allow trialists to change the nature of the trial (eg, stop early for success or futility, drop an underperforming trial arm, incorporate data on historical controls, etc) while it is still running. In this article, we review a broad collection of Bayesian techniques useful in rare disease research, indicating the benefits and risks associated with each. We begin with relatively innocuous methods for combining information from RCTs and proceed on through increasingly innovative approaches that borrow strength from increasingly heterogeneous and less carefully curated data sources. We also offer 2 examples from the very recent literature illustrating how clinical pharmacology principles can make important contributions to such designs, confirming the interdisciplinary nature of this work.

Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data

The 21^st Century Cures Act requires FDA to expand its use of real-world evidence (RWE) to support approval of previously approved drugs for new disease indications and post-marketing study requirements. To address this need in neonates, the FDA and the Critical Path Institute (C-Path) established the International Neonatal Consortium (INC) to advance regulatory science and expedite neonatal drug development. FDA recently provided funding for INC to generate RWE to support regulatory decision making in neonatal drug development. One study is focused on developing a validated definition of bronchopulmonary dysplasia (BPD) in neonates. BPD is difficult to diagnose with diverse disease trajectories and few viable treatment options. Despite intense research efforts, limited understanding of the underlying disease pathobiology and disease projection continues in the context of a computable phenotype. It will be important to determine if: 1) a large, multisource aggregation of real-world data (RWD) will allow identification of validated risk factors and surrogate endpoints for BPD, and 2) the inclusion of these simulations will identify risk factors and surrogate endpoints for studies to prevent or treat BPD and its related long-term complications. The overall goal is to develop qualified, fit-for-purpose disease progression models which facilitate credible trial simulations while quantitatively capturing mechanistic relationships relevant for disease progression and the development of future treatments. The extent to which neonatal RWD can inform these models is unknown and its appropriateness cannot be guaranteed. A component of this approach is the critical evaluation of the various RWD sources for context-of use (COU)-driven models. The present manuscript defines a landscape of the data including targeted literature searches and solicitation of neonatal RWD sources from international stakeholders; analysis plans to develop a family of models of BPD in neonates, leveraging previous clinical trial experience and real-world patient data is also described.

Early clinical and pre-clinical therapy development in Nemaline myopathy

INTRODUCTION

Nemaline myopathies (NM) represent a group of clinically and genetically heterogeneous congenital muscle disorders with the common denominator of nemaline rods on muscle biopsy. NEB and ACTA1 are the most common causative genes. Currently, available treatments are supportive.

AREAS COVERED

We explored experimental treatments for NM, identifying at least eleven mainly pre-clinical approaches utilizing murine and/or human muscle cells. These approaches target either i) the causative gene or associated genes implicated in the same pathway; ii) pathophysiologically relevant biochemical mechanisms such as calcium/myosin regulation of muscle contraction; iii) myogenesis; iv) other therapies that improve or optimize muscle function more generally; v) and/or combinations of the above. The scope and efficiency of these attempts is diverse, ranging from gene-specific effects to those widely applicable to all NM-associated genes.

EXPERT OPINION

The wide range of experimental therapies currently under consideration for NM is promising. Potential translation into clinical use requires consideration of additional factors such as the potential muscle type specificity as well as the possibility of gene expression remodeling. Challenges in clinical translation include the rarity and heterogeneity of genotypes, phenotypes, and disease trajectories, as well as the lack of longitudinal natural history data and validated outcomes and biomarkers.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

Risdiplam: an investigational motor neuron-2 (SMN-2) splicing modifier for spinal muscular atrophy (SMA)

INTRODUCTION

Spinal muscular atrophy (SMA) is a rare autosomal recessive neuromuscular disease which is characterized by muscle atrophy and early death in most patients. Risdiplam is the third overall and first oral drug approved for SMA with disease-modifying potential. Risdiplam acts as a survival motor neuron 2 (SMN2) pre-mRNA splicing modifier with satisfactory safety and efficacy profile. This review aims to critically appraise the place of risdiplam in the map of SMA therapeutics.

AREAS COVERED

This review gives an overview of the current market for SMA and presents the mechanism of action and the pharmacological properties of risdiplam. It also outlines the development of risdiplam from early preclinical stages through to the most recently published results from phase 2/3 clinical trials. Risdiplam has proved its efficacy in pivotal trials for SMA Types 1, 2, and 3 with a satisfactory safety profile.

EXPERT OPINION

In the absence of comparative data with the other two approved drugs, the role of risdiplam in the treatment algorithm of affected individuals is examined in three different patient populations based on the age and diagnosis method (newborn screening or clinical, symptom-driven diagnosis). Long-term data and real-world data will play a fundamental role in its future.

Zozus M, Jiang X, Brimhall BB, Windham AK, Schmidt S, Visweswaran S, Ye Y, Goodrum H, Ling Y, Barapatre S, Becich MJ. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

Lessons Learned from Discontinued Clinical Developments in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked condition caused by a deficiency of functional dystrophin protein. Patients experience progressive muscle weakness, cardiomyopathy and have a decreased life expectancy. Standards of care, including treatment with steroids, and multidisciplinary approaches have extended the life expectancy and improved the quality of life of patients. In the last 30 years, several compounds have been assessed in preclinical and clinical studies for their ability to restore functional dystrophin levels or to modify pathways involved in DMD pathophysiology. However, there is still an unmet need with regards to a disease-modifying treatment for DMD and the attrition rate between early-phase and late-phase clinical development remains high. Currently, there are 40 compounds in clinical development for DMD, including gene therapy and antisense oligonucleotides for exon skipping. Only five of them have received conditional approval in one jurisdiction subject to further proof of efficacy. In this review, we present data of another 16 compounds that failed to complete clinical development, despite positive results in early phases of development in some cases. We examine the reasons for the high attrition rate and we suggest solutions to avoid similar mistakes in the future.

The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness

BACKGROUND

Rare diseases are estimated to affect 150-350 million people worldwide. With advances in next generation sequencing, the number of known disease-causing genes has increased significantly, opening the door for therapy development. Rare disease research has therefore pivoted from gene discovery to the exploration of potential therapies. With impending clinical trials on the horizon, researchers are in urgent need of natural history studies to help them identify surrogate markers, validate outcome measures, define historical control patients, and design therapeutic trials.

RESULTS

We customized a browser-accessible multi-modal (e.g. genetics, imaging, behavioral, patient-determined outcomes) database to increase cohort sizes, identify surrogate markers, and foster international collaborations. Ninety data entry forms were developed including family, perinatal, developmental history, clinical examinations, diagnostic investigations, neurological evaluations (i.e. spasticity, dystonia, ataxia, etc.), disability measures, parental stress, and quality of life. A customizable clinical letter generator was created to assist in continuity of patient care.

CONCLUSIONS

Small cohorts and underpowered studies are a major challenge for rare disease research. This online, rare disease database will be accessible from all over the world, making it easier to share and disseminate data. We have outlined the methodology to become Title 21 Code of Federal Regulations Part 11 Compliant, which is a requirement to use electronic records as historical controls in clinical trials in the United States. Food and Drug Administration compliant databases will be life-changing for patients and families when historical control data is used for emerging clinical trials. Future work will leverage these tools to delineate the natural history of several rare diseases and we are confident that this database will be used on a larger scale to improve care for patients affected with rare diseases.