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Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities.

ABSTRACT: Huntington's disease is the most frequent autosomal dominant neurodegenerative disorder; however, no disease-modifying interventions are available for patients with this disease. The molecular pathogenesis of Huntington's disease is complex, with toxicity that arises from full-length expanded huntingtin and N-terminal fragments of huntingtin, which are both prone to misfolding due to proteolysis; aberrant intron-1 splicing of the HTT gene; and somatic expansion of the CAG repeat in the HTT gene. Potential interventions for Huntington's disease include therapies targeting huntingtin DNA and RNA, clearance of huntingtin protein, DNA repair pathways, and other treatment strategies targeting inflammation and cell replacement. The early termination of trials of the antisense oligonucleotide tominersen suggest that it is time to reflect on lessons learned, where the field stands now, and the challenges and opportunities for the future.

SUBMITTER: Tabrizi SJ

PROVIDER: S-EPMC7613206 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities.

Tabrizi Sarah J SJ Estevez-Fraga Carlos C van Roon-Mom Willeke M C WMC Flower Michael D MD Scahill Rachael I RI Wild Edward J EJ Muñoz-Sanjuan Ignacio I Sampaio Cristina C Rosser Anne E AE Leavitt Blair R BR

The Lancet. Neurology 20220701 7

Huntington's disease is the most frequent autosomal dominant neurodegenerative disorder; however, no disease-modifying interventions are available for patients with this disease. The molecular pathogenesis of Huntington's disease is complex, with toxicity that arises from full-length expanded huntingtin and N-terminal fragments of huntingtin, which are both prone to misfolding due to proteolysis; aberrant intron-1 splicing of the HTT gene; and somatic expansion of the CAG repeat in the HTT gene. ...[more]

PMID: 35716694

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Project description:BackgroundPromising disease modifying therapies for Huntington's disease are now entering pivotal trials, raising questions of what patients and families consider successful outcomes. Consistent with an ongoing movement to incorporate patient preferences into the development of new therapies, we conducted a pilot study to assess Huntington's disease community views on emerging DMTs to assist in planning large-scale studies of patient preferences.MethodsSemi-structured interviews were conducted with members of the Huntington's community (manifest disease, at-risk, and family/caregivers). Participants were asked which symptoms they believed should be targeted with novel treatments, as well as potential benefits and tradeoffs of delaying symptom onset versus prolonging late-stage disease.ResultsParticipants (N = 14) emphasized the need for treatments improving cognitive and/or behavioral symptoms. Many wanted treatments that delayed symptom onset up to 5-10 years, though some considered shorter delays acceptable due to potential value in advancing research to help future generations. Concern regarding potential for prolonging later-stage disease was variable, with some participants uncertain if they would want a treatment that delayed onset but prolonged later-stage disease. Others stated that any delay in onset would be desirable, regardless of potential prolongation of later stage disease.DiscussionThis study demonstrates a breadth of opinions among the Huntington's disease community surrounding both the benefits and complex tradeoffs that might occur with disease modifying treatments. These preliminary findings will inform future large-scale studies of attitudes toward disease modifying treatments, which may ultimately guide the design and outcome measure selection for clinical trials.HighlightsIn-depth interviews with the Huntington's disease community were used to explore patient and family preferences regarding potential disease modifying therapies. Many wanted symptom delay of 5-10 years, though some considered shorter delays acceptable for altruistic reasons. Opinions on trade-offs varied, suggesting larger preference studies are needed to inform trial design.

Project description:South Africa, like many other developing countries, stands to benefit from novel diagnostics and drugs developed by pharmacogenomics guidance due to high prevalence of disease burden in the region. This includes both communicable (e.g., HIV/AIDS and tuberculosis) and non-communicable (e.g., diabetes and cardiovascular) diseases. For example, although only 0.7% of the world's population lives in South Africa, the country carries 17% of the global HIV/AIDS burden and 5% of the global tuberculosis burden. Nobel Peace Prize Laureate Archbishop Emeritus Desmond Tutu has coined the term Rainbow Nation, referring to a land of wealth in its many diverse peoples and cultures. It is now timely and necessary to reflect on how best to approach new genomics biotechnologies in a manner that carefully considers the public health needs and extant disease burden in the region. The aim of this paper is to document and review the advances in pharmacogenomics in South Africa and importantly, to evaluate the direction that future research should take. Previous research has shown that the populations in South Africa exhibit unique allele frequencies and novel genetic variation in pharmacogenetically relevant genes, often differing from other African and global populations. The high level of genetic diversity, low linkage disequilibrium and the presence of rare variants in these populations question the feasibility of the use of current commercially available genotyping platforms, and may partially account for genotype-phenotype discordance observed in past studies. However, the employment of high throughput technologies for genomic research, within the context of large clinical trials, combined with interdisciplinary studies and appropriate regulatory guidelines, should aid in acceleration of pharmacogenomic discoveries in high priority therapeutic areas in South Africa. Finally, we suggest that projects such as the H3Africa Initiative, the SAHGP and PGENI should play an integral role in the coordination of genomic research in South Africa, but also other African countries, by providing infrastructure and capital to local researchers, as well as providing aid in addressing the computational and statistical bottlenecks encountered at present.

Project description:Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

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Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities.

Publications

Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities.

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