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Mitra A, Tania N, Ahmed MA, Rayad N, Krishna R, Albusaysi S, Bakhaidar R, Shang E, Burian M, Martin-Pozo M, Younis IR. New Horizons of Model Informed Drug Development in Rare Diseases Drug Development. Clin Pharmacol Ther 2024. [PMID: 38989644 DOI: 10.1002/cpt.3366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 06/23/2024] [Indexed: 07/12/2024]
Abstract
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.
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Affiliation(s)
- Amitava Mitra
- Clinical Pharmacology, Kura Oncology Inc., Boston, Massachusetts, USA
| | - Nessy Tania
- Translational Clinical Sciences, Pfizer Research and Development, Cambridge, Massachusetts, USA
| | - Mariam A Ahmed
- Quantitative Clinical Pharmacology, Takeda Development Center, Cambridge, Massachusetts, USA
| | - Noha Rayad
- Clinical Pharmacology, Modeling and Simulation, Parexel International (Canada) LTD, Mississauga, Ontario, Canada
| | - Rajesh Krishna
- Certara Drug Development Solutions, Certara USA, Inc., Princeton, New Jersey, USA
| | - Salwa Albusaysi
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rana Bakhaidar
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Elizabeth Shang
- Global Regulatory Affairs and Clinical Safety, Merck &Co., Inc., Rahway, New Jersey, USA
| | - Maria Burian
- Clinical Science, UCB Biopharma SRL, Braine-l'Alleud, Belgium
| | - Michelle Martin-Pozo
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Islam R Younis
- Quantitative Pharmacology and Pharmacometrics, Merck &Co., Inc., Rahway, New Jersey, USA
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Iddrisu AK, A. Amikiya E, Kwame Bukari F. Spatio-temporal characteristics of Tuberculosis in Ghana. F1000Res 2024; 11:200. [PMID: 38868172 PMCID: PMC11167329 DOI: 10.12688/f1000research.109053.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/17/2024] [Indexed: 06/14/2024] Open
Abstract
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.
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Affiliation(s)
- Abdul-Karim Iddrisu
- Mathematics and Staistics, University of Energy and Natural Resources, Sunyani, Brong Ahafo, +233, Ghana
| | - Emmanuel A. Amikiya
- Department of Management Science, Ghana Institute of Management and Public Administration, Accra, Greater Accra, +233, Ghana
| | - Francis Kwame Bukari
- Mathematics and Staistics, University of Energy and Natural Resources, Sunyani, Brong Ahafo, +233, Ghana
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3
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Anwar S, Yokota T. Navigating the Complex Landscape of Fibrodysplasia Ossificans Progressiva: From Current Paradigms to Therapeutic Frontiers. Genes (Basel) 2023; 14:2162. [PMID: 38136984 PMCID: PMC10742611 DOI: 10.3390/genes14122162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 12/24/2023] Open
Abstract
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.
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Affiliation(s)
| | - Toshifumi Yokota
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2H7, Canada;
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Ara T, Kitamura H. Development of a Predictive Statistical Pharmacological Model for Local Anesthetic Agent Effects with Bayesian Hierarchical Model Parameter Estimation. MEDICINES (BASEL, SWITZERLAND) 2023; 10:61. [PMID: 37999201 PMCID: PMC10672774 DOI: 10.3390/medicines10110061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/28/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023]
Abstract
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.
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Affiliation(s)
- Toshiaki Ara
- Department of Pharmacology, Matsumoto Dental University, 1780 Gobara Hirooka, Shiojiri 399-0781, Nagano, Japan
| | - Hiroyuki Kitamura
- Matsumoto Dental University Hospital, 1780 Gobara Hirooka, Shiojiri 399-0781, Nagano, Japan
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Traschütz A, Adarmes-Gómez AD, Anheim M, Baets J, Brais B, Gagnon C, Gburek-Augustat J, Doss S, Hanağası HA, Kamm C, Klivenyi P, Klockgether T, Klopstock T, Minnerop M, Münchau A, Renaud M, Santorelli FM, Schöls L, Thieme A, Vielhaber S, van de Warrenburg BP, Zanni G, Hilgers RD, Synofzik M. Responsiveness of the Scale for the Assessment and Rating of Ataxia and Natural History in 884 Recessive and Early Onset Ataxia Patients. Ann Neurol 2023; 94:470-485. [PMID: 37243847 DOI: 10.1002/ana.26712] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 04/04/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
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.
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Affiliation(s)
- Andreas Traschütz
- Research Division "Translational Genomics of Neurodegenerative Diseases," Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Astrid D Adarmes-Gómez
- Movement Disorders Unit, Department of Neurology and Clinical Neurophysiology, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
- Center for Biomedical Research Network on Neurodegenerative Diseases, Madrid, Spain
| | - Mathieu Anheim
- Department of Neurology, Hautepierre Hospital, University Hospitals of Strasbourg, Strasbourg, France
- Federation of Translational Medicine of Strasbourg, University of Strasbourg, Strasbourg, France
- Institute of Genetics and Molecular and Cellular Biology, INSERM-U964/CNRS-UMR7104/University of Strasbourg, Illkirch, France
| | - Jonathan Baets
- Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- Laboratory of Neuromuscular Pathology, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Neuromuscular Reference Center, Department of Neurology, Antwerp University Hospital, Antwerp, Belgium
| | - Bernard Brais
- Department of Neurology, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Cynthia Gagnon
- CHUS Research Center and Health and Social Services Center of Saguenay-Lac-Saint-Jean, Faculty of Medicine, University of Sherbrooke, Quebec, Quebec, Canada
| | - Janina Gburek-Augustat
- Division of Neuropediatrics, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
| | - Sarah Doss
- Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Free University of Berlin, Humboldt University of Berlin, Berlin, Germany
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Haşmet A Hanağası
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Christoph Kamm
- Department of Neurology, University of Rostock, Rostock, Germany
| | - Peter Klivenyi
- Interdisciplinary Excellence Center, Department of Neurology, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
| | - Thomas Klockgether
- Department of Neurology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Thomas Klopstock
- Department of Neurology, Friedrich Baur Institute, Ludwig Maximilian University of Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
| | - Martina Minnerop
- Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Alexander Münchau
- Institute of Systems Motor Science, Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Mathilde Renaud
- Clinical Genetics Service, CHRU of Nancy, Nancy, France
- INSERM-U1256 NGERE, University of Lorraine, Nancy, France
| | | | - Ludger Schöls
- Research Division "Translational Genomics of Neurodegenerative Diseases," Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Andreas Thieme
- Department of Neurology and Center for Translational Neuro and Behavioral Sciences, Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Stefan Vielhaber
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Bart P van de Warrenburg
- Department of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ginevra Zanni
- Unit of Neuromuscular and Neurodegenerative Disorders, Department of Neurosciences, Bambino Gesù Childrens' Hospital, IRCCS, Rome, Italy
| | | | - Matthis Synofzik
- Research Division "Translational Genomics of Neurodegenerative Diseases," Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
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Bouma S, Cobben N, Bouman K, Gaytant M, van de Biggelaar R, van Doorn J, Reumers SFI, Voet NB, Doorduin J, Erasmus CE, Kamsteeg EJ, Jungbluth H, Wijkstra P, Voermans NC. Respiratory features of centronuclear myopathy in the Netherlands. Neuromuscul Disord 2023; 33:580-588. [PMID: 37364426 DOI: 10.1016/j.nmd.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/28/2023]
Abstract
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.
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Affiliation(s)
- Sietse Bouma
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nicolle Cobben
- Department of Pulmonary Diseases & Home Mechanical Ventilation, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Karlijn Bouman
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Michael Gaytant
- Center for Home Mechanical Ventilation, Department of Pulmonology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ries van de Biggelaar
- Department of Pulmonary Diseases & Home Mechanical Ventilation, Erasmus MC, Rotterdam, the Netherlands
| | - Jeroen van Doorn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Stacha F I Reumers
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nicoline Bm Voet
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Rehabilitation Center Klimmendaal, Arnhem, the Netherlands
| | - Jonne Doorduin
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Corrie E Erasmus
- Department of Paediatric Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center - Amalia Children's Hospital, Nijmegen, the Netherlands
| | - Erik-Jan Kamsteeg
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Heinz Jungbluth
- Department of Paediatric Neurology, Neuromuscular Service, Evelina's Children Hospital, Guy's & St. Thomas' Hospital NHS Foundation Trust, London, UK; Randall Centre for Cell and Molecular Biophysics, Muscle Signalling Section, FoLSM, King's College, London, UK
| | - Peter Wijkstra
- Department of Pulmonary Diseases & Home Mechanical Ventilation, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Centre Groningen, the Netherlands
| | - Nicol C Voermans
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
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Aartsma-Rus A, van Roon-Mom W, Lauffer M, Siezen C, Duijndam B, Coenen-de Roo T, Schüle R, Synofzik M, Graessner H. Development of tailored splice-switching oligonucleotides for progressive brain disorders in Europe: development, regulation, and implementation considerations. RNA (NEW YORK, N.Y.) 2023; 29:446-454. [PMID: 36669889 PMCID: PMC10019362 DOI: 10.1261/rna.079540.122] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 01/12/2023] [Indexed: 06/01/2023]
Abstract
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.
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Affiliation(s)
- Annemieke Aartsma-Rus
- Dutch Center for RNA Therapeutics and Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- 1 Mutation 1 Medicine, 72076 Tübingen, Germany
| | - Willeke van Roon-Mom
- Dutch Center for RNA Therapeutics and Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- 1 Mutation 1 Medicine, 72076 Tübingen, Germany
| | - Marlen Lauffer
- Dutch Center for RNA Therapeutics and Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | | | - Britt Duijndam
- Medicines Evaluation Board, 3531 AH Utrecht, The Netherlands
| | - Tineke Coenen-de Roo
- Dutch Center for RNA Therapeutics and Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Animal and Transgenesis Facility, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Rebecca Schüle
- 1 Mutation 1 Medicine, 72076 Tübingen, Germany
- Division of Neurodegenerative Diseases, Department of Neurology, University of Heidelberg Medical Center, 69120 Heidelberg, Germany
- Center for Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany
- European Reference Network for Rare Neurological Diseases
| | - Matthis Synofzik
- 1 Mutation 1 Medicine, 72076 Tübingen, Germany
- Center for Neurology and Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie-Institute of Clinical Brain Research & Center of Neurology, 72076 Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- European Reference Network for Rare Neurological Diseases
| | - Holm Graessner
- 1 Mutation 1 Medicine, 72076 Tübingen, Germany
- Institute for Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany
- Center for Rare Diseases, University Hospital Tübingen, 72076 Tübingen, Germany
- European Reference Network for Rare Neurological Diseases
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8
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de Oliveira CM, Leotti VB, Cappelli AH, Rocha AG, Ecco G, Bolzan G, Kersting N, Saraiva-Pereira ML, Jardim LB. Progression of Clinical and Eye Movement Markers in Preataxic Carriers of Machado-Joseph Disease. Mov Disord 2023; 38:26-34. [PMID: 36129443 DOI: 10.1002/mds.29226] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 01/21/2023] Open
Abstract
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.
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Affiliation(s)
- Camila Maria de Oliveira
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Vanessa Bielefeldt Leotti
- Departamento de Estatística, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Programa de Pós-Graduação em Epidemiologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Amanda Henz Cappelli
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gabriela Ecco
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gabriela Bolzan
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathalia Kersting
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Maria-Luiza Saraiva-Pereira
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Laura Bannach Jardim
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Departamento de Medicina Interna, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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9
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Carlin BP, Nollevaux F. Bayesian Complex Innovative Trial Designs (CIDs) and Their Use in Drug Development for Rare Disease. J Clin Pharmacol 2022; 62 Suppl 2:S56-S71. [PMID: 36461743 DOI: 10.1002/jcph.2132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/01/2022] [Indexed: 12/04/2022]
Abstract
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.
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10
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Barrett JS, Cala Pane M, Knab T, Roddy W, Beusmans J, Jordie E, Singh K, Davis JM, Romero K, Padula M, Thebaud B, Turner M. Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data. Front Pharmacol 2022; 13:988974. [PMID: 36313352 PMCID: PMC9597633 DOI: 10.3389/fphar.2022.988974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/21/2022] [Indexed: 11/27/2022] Open
Abstract
The 21st 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.
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Affiliation(s)
- Jeffrey S. Barrett
- Critical Path Institute, Tucson, AZ, United States
- *Correspondence: Jeffrey S. Barrett,
| | | | - Timothy Knab
- Metrum Research Group, Tariffville, CT, United States
| | | | - Jack Beusmans
- Metrum Research Group, Tariffville, CT, United States
| | - Eric Jordie
- Metrum Research Group, Tariffville, CT, United States
| | | | - Jonathan Michael Davis
- Tufts Medical Center and the Tufts Clinical and Translational Science Institute, Boston, MA, United States
| | - Klaus Romero
- Critical Path Institute, Tucson, AZ, United States
| | - Michael Padula
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Bernard Thebaud
- Department of Pediatrics, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Mark Turner
- Department of Women’s and Children’s Health Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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11
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Fisher G, Mackels L, Markati T, Sarkozy A, Ochala J, Jungbluth H, Ramdas S, Servais L. Early clinical and pre-clinical therapy development in Nemaline myopathy. Expert Opin Ther Targets 2022; 26:853-867. [PMID: 36524401 DOI: 10.1080/14728222.2022.2157258] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
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.
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Affiliation(s)
- Gemma Fisher
- MDUK Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Laurane Mackels
- MDUK Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK.,Neuromuscular Reference Center, University and University Hospital of Liège, Liège, Belgium
| | - Theodora Markati
- MDUK Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Anna Sarkozy
- Dubowitz Neuromuscular Centre, UCL Great Ormond Street Hospital, Institute of Child Health, London, UK
| | - Julien Ochala
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Heinz Jungbluth
- Department of Paediatric Neurology - Neuromuscular Service, Evelina Children's Hospital, Guy's & St Thomas' NHS Foundation Trust, London, UK.,Randall Centre for Cell and Molecular Biophysics, Muscle Signalling Section, Faculty of Life Sciences and Medicine (FoLSM), King's College London, London, UK
| | - Sithara Ramdas
- MDUK Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK.,Department of Paediatric Neurology, John Radcliffe Hospital, Oxford, UK
| | - Laurent Servais
- MDUK Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK.,Neuromuscular Reference Center, University and University Hospital of Liège, Liège, Belgium
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12
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Barrett JS, Nicholas T, Azer K, Corrigan BW. Role of Disease Progression Models in Drug Development. Pharm Res 2022; 39:1803-1815. [PMID: 35411507 PMCID: PMC9000925 DOI: 10.1007/s11095-022-03257-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/05/2022] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Jeffrey S. Barrett
- Rare Disease Cures Accelerator Data Analytics Platform, Critical Path Institute, Tuscon, AZ 85718 USA
| | - Tim Nicholas
- Global Product Development, Pfizer Inc, 445 Eastern Point Rd, Groton, CT 06340 USA
| | - Karim Azer
- Axcella Therapeutics, 840 Memorial Drive, Cambridge, MA 02139 USA
| | - Brian W. Corrigan
- Global Product Development, Pfizer Inc, 445 Eastern Point Rd, Groton, CT 06340 USA
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13
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Emerging therapies for Duchenne muscular dystrophy. Lancet Neurol 2022; 21:814-829. [DOI: 10.1016/s1474-4422(22)00125-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 02/21/2022] [Accepted: 03/18/2022] [Indexed: 12/11/2022]
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14
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Kidwell KM, Roychoudhury S, Wendelberger B, Scott J, Moroz T, Yin S, Majumder M, Zhong J, Huml RA, Miller V. Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles. Orphanet J Rare Dis 2022; 17:186. [PMID: 35526036 PMCID: PMC9077995 DOI: 10.1186/s13023-022-02342-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Kelley M Kidwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
| | | | | | - John Scott
- Food and Drug Administration, Washington, DC, USA
| | | | - Shaoming Yin
- Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | | | | | | | - Veronica Miller
- Forum for Collaborative Research, University of California School of Public Health, Berkeley, CA, USA
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15
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Markati T, Fisher G, Ramdas S, Servais L. Risdiplam: an investigational motor neuron-2 (SMN-2) splicing modifier for spinal muscular atrophy (SMA). Expert Opin Investig Drugs 2022; 31:451-461. [PMID: 35316106 DOI: 10.1080/13543784.2022.2056836] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
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.
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Affiliation(s)
- Theodora Markati
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, UK.,Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Gemma Fisher
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, UK.,Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Sithara Ramdas
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, UK.,Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Laurent Servais
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, UK.,Oxford University Hospitals NHS Foundation Trust, Oxford, UK.,Division of Child Neurology, Centre de Références des Maladies Neuromusculaires, Department of Pediatrics, University Hospital Liège & University of Liège, Belgium
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16
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Muntoni F, Signorovitch J, Sajeev G, Goemans N, Wong B, Tian C, Mercuri E, Done N, Wong H, Moss J, Yao Z, Ward SJ, Manzur A, Servais L, Niks EH, Straub V, de Groot IJM, McDonald C. Real-world and natural history data for drug evaluation in Duchenne muscular dystrophy: suitability of the North Star Ambulatory Assessment for comparisons with external controls. Neuromuscul Disord 2022; 32:271-283. [DOI: 10.1016/j.nmd.2022.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
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17
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Bernstam EV, Shireman PK, Meric‐Bernstam F, N. 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. Clin Transl Sci 2022; 15:309-321. [PMID: 34706145 PMCID: PMC8841416 DOI: 10.1111/cts.13175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/01/2021] [Indexed: 01/12/2023] Open
Abstract
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.
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Affiliation(s)
- Elmer V. Bernstam
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Division of General Internal MedicineDepartment of Internal MedicineMcGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Paula K. Shireman
- Departments of Surgery and MicrobiologyImmunology & Molecular GeneticsUniversity of Texas Health San AntonioSan AntonioTexasUSA
- University HealthSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Funda Meric‐Bernstam
- Department of Investigational Cancer TherapeuticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Meredith N. Zozus
- Division of Clinical Research InformaticsDepartment of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Xiaoqian Jiang
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Bradley B. Brimhall
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Ashley K. Windham
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Susanne Schmidt
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Ye Ye
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Heath Goodrum
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Yaobin Ling
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Seemran Barapatre
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Michael J. Becich
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
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18
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Markati T, De Waele L, Schara-Schmidt U, Servais L. Lessons Learned from Discontinued Clinical Developments in Duchenne Muscular Dystrophy. Front Pharmacol 2021; 12:735912. [PMID: 34790118 PMCID: PMC8591262 DOI: 10.3389/fphar.2021.735912] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 10/12/2021] [Indexed: 02/04/2023] Open
Abstract
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.
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Affiliation(s)
- Theodora Markati
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Liesbeth De Waele
- KU Leuven Department of Development and Regeneration, Leuven, Belgium
- Department of Paediatric Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Urlike Schara-Schmidt
- Department of Pediatric Neurology, Center for Neuromuscular Diseases, Center for Translational Neuro- and Behavioral Sciences, University Duisburg-Essen, Essen, Germany
| | - Laurent Servais
- MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
- Division of Child Neurology, Reference Center for Neuromuscular Disease, Centre Hospitalier Régional de Références des Maladies Neuromusculaires, Department of Pediatrics, University Hospital Liège, Liège, Belgium
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19
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Monseur A, Carlin BP, Boulanger B, Seferian A, Servais L, Freitag C, Thielemans L, Gidaro T, Gargaun E, Chê V, Schara U, Gangfuß A, D’Amico A, Dowling JJ, Darras BT, Daron A, Hernandez A, de Lattre C, Arnal JM, Mayer M, Cuisset JM, Vuillerot C, Fontaine S, Bellance R, Biancalana V, Buj-Bello A, Hogrel JY, Landy H, Amburgey K, Andres B, Bertini E, Cardas R, Denis S, Duchêne D, Latournerie V, Reguiba N, Tsuchiya E, Wallgren-Pettersson C. Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09323-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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20
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Spahr A, Rosli Z, Legault M, Tran LT, Fournier S, Toutounchi H, Darbelli L, Madjar C, Lucia C, St-Jean ML, Das S, Evans AC, Bernard G. The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness. Orphanet J Rare Dis 2021; 16:328. [PMID: 34301277 PMCID: PMC8299589 DOI: 10.1186/s13023-021-01953-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/11/2021] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Aaron Spahr
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Zaliqa Rosli
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Mélanie Legault
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Luan T Tran
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Simon Fournier
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Helia Toutounchi
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Lama Darbelli
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Cécile Madjar
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Cassandra Lucia
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Marie-Lou St-Jean
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
- Department of Pediatrics, McGill University, Montréal, Québec, Canada.
- Department of Human Genetics, McGill University, Montréal, Québec, Canada.
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada.
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada.
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