A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design

Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial

BACKGROUND

In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.

OBJECTIVES

To improve stratification of Huntington's disease individuals for clinical trials.

METHODS

We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.

RESULTS

The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).

CONCLUSIONS

This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Unraveling progression subtypes in people with Huntington's disease

Background

Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.

Methods

Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.

Results

Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only.

Conclusion

In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM)

Background

Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states.

Objective

To generate a temporal map of discriminatory clinical measures across the IHDPM states.

Methods

We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen's effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790).

Results

Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items - most notably TMS - showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power.

Conclusion

By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci.

Harnessing artificial intelligence to improve clinical trial design

Zhang et al. discuss how artificial intelligence (AI) can be used to optimize clinical trial design and potentially boost the success rate of clinical trials. AI has unparalleled potential to leverage real-world data and unlock valuable insights for innovative trial design.

Uncovering the Genetic and Molecular Features of Huntington's Disease in Northern Colombia

Huntington's disease (HD) is a genetic disorder caused by a CAG trinucleotide expansion in the huntingtin (HTT) gene. Juan de Acosta, Atlántico, a city located on the Caribbean coast of Colombia, is home to the world's second-largest HD pedigree. Here, we include 291 descendants of this pedigree with at least one family member with HD. Blood samples were collected, and genomic DNA was extracted. We quantified the HTT CAG expansion using an amplicon sequencing protocol. The genetic heterogeneity was measured as the ratio of the mosaicism allele's read peak and the slippage ratio of the allele's read peak from our sequence data. The statistical and bioinformatic analyses were performed with a significance threshold of p < 0.05. We found that the average HTT CAG repeat length in all participants was 21.91 (SD = 8.92). Of the 291 participants, 33 (11.3%, 18 females) had a positive molecular diagnosis for HD. Most affected individuals were adults, and the most common primary and secondary alleles were 17/7 (CAG/CCG) and 17/10 (CAG/CCG), respectively. The mosaicism increased with age in the participants with HD, while the slippage analyses revealed differences by the HD allele type only for the secondary allele. The slippage tended to increase with the HTT CAG repeat length in the participants with HD, but the increase was not statistically significant. This study analyzed the genetic and molecular features of 291 participants, including 33 with HD. We found that the mosaicism increased with age in the participants with HD, particularly for the secondary allele. The most common haplotype was 17/7_17/10. The slippage for the secondary allele varied by the HD allele type, but there was no significant difference in the slippage by sex. Our findings offer valuable insights into HD and could have implications for future research and clinical management.

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis and driving capability prediction

BACKGROUND

In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington's disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world's largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments.

RESULTS

Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main .

CONCLUSIONS

Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Subtyping monogenic disorders: Huntington disease

Huntington disease is a highly disabling neurodegenerative disease characterized by psychiatric, cognitive, and motor deficits. The causal genetic mutation in huntingtin (Htt, also known as IT15), located on chromosome 4p16.3, leads to an expansion of a triplet coding for polyglutamine. The expansion is invariably associated with the disease when >39 repeats. Htt encodes for the protein huntingtin (HTT), which carries out many essential biological functions in the cell, in particular in the nervous system. The precise mechanism of toxicity is not known. Based on a one-gene-one-disease framework, the prevailing hypothesis ascribes toxicity to the universal aggregation of HTT. However, the aggregation process into mutant huntingtin (mHTT) is associated with a reduction of the levels of wild-type HTT. A loss of wild-type HTT may plausibly be pathogenic, contributing to the disease onset and progressive neurodegeneration. Moreover, many other biological pathways are altered in Huntington disease, such as in the autophagic system, mitochondria, and essential proteins beyond HTT, potentially explaining biological and clinical differences among affected individuals. As one gene does not mean one disease, future efforts at identifying specific Huntington subtypes are important to design biologically tailored therapeutic approaches that correct the corresponding biological pathways-rather than continuing to exclusively target the common denominator of HTT aggregation for elimination.

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.

Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Considerations for clinical trial design and conduct in the evaluation of novel advanced therapeutics in neurodegenerative disease

The recent advances in the development of potentially disease modifying cell and gene therapies for neurodegenerative disease has resulted in the production of a number of promising novel therapies which are now moving forward to clinical evaluation. The robust evaluation of these therapies pose a significant number of challenges when compared to more traditional evaluations of pharmacotherapy, which is the current mainstay of neurodegenerative disease symptom management. Indeed, there is an inherent complexity in the design and conduct of these trials at multiple levels. Here we discuss specific aspects requiring consideration in the context of investigating novel cell and gene therapies for neurodegenerative disease. This extends to overarching trial designs that could be employed and the factors that underpin design choices such outcome assessments, participant selection and methods for delivery of cell and gene therapies. We explore methods of data collection that may improve efficiency in trials of cell and gene therapy to maximize data sharing and collaboration. Lastly, we explore some of the additional context beyond efficacy evaluations that should be considered to ensure implementation across relevant healthcare settings.