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Future shows great potential for neurological language assessments in clinical trials.
Alzheimer’s disease (AD), as well as other neurodegenerative dementias, such as frontotemporal dementia (FTD), can be characterized by progressively worsening deficits in several cognitive domains, including language. Patients with these conditions may demonstrate different speech patterns, such as difficulty in retrieving words (anomia), reduced information content, sentence comprehension deficits, and a lack of cohesion in discourse. Despite the prevalence of language characteristics as a symptom of these disorders, tools used to identify the extent of cognitive impairment in the study of AD and FTD do not typically monitor speech or rely on subjective ratings. Growing awareness of the opportunities that language evaluation can bring to clinical research is driving interest in speech-based digital biomarkers to evaluate changes in cognition and achieve more sensitive detection of the impact of treatments.
Investigational drugs for AD and FTD have an extensive history of failing to demonstrate efficacy in Phase II and Phase III clinical trials. In trying to find reasons for these setbacks there is agreement that the limited sensitivity of current clinical trial endpoints may be a factor. Most of the standard primary endpoints used in AD and FTD research are clinical measures of cognition and/or function, such as the Clinical Dementia Rating (CDR) scale. While global rating scales serve a helpful purpose in providing a quantifiable clinical judgement about disease severity, there is an awareness that these tests do not adequately capture slight changes or improvements in a patient’s condition.
Putting questions on sensitivity aside, these standard approaches to testing are also hugely complex, difficult to administer and time intensive. A single test can take up to an hour to complete. Most also need to be administered by a clinician or psychometrist, meaning that patients are unable to complete them remotely. Repeatability may also pose challenges due to the potential for non-memory impaired patients to learn what to expect if these same tests are revisited regularly.
With the pressing need to develop and evaluate disease-modifying treatments for these diseases, there is an appetite from researchers for new tools and more sensitive means of measuring function and cognition in AD and FTD patients. Language represents a huge opportunity. As more sophisticated, automated, and objective ways of analyzing speech come into the frame, there is potential to gain insight into the language function of patients in a way that has historically not been achievable.
The adoption of speech as a biomarker in clinical research has the potential to capture more subtle speech and language changes in patients with cognitive impairment. People with AD and FTD may struggle to recall words, they may resort to basic vocabulary, construct simpler sentences, and speak at a slower rate. While clinicians and investigators may notice subtle language problems in patients at the early stages of these diseases, they do not currently have the tools to objectively characterize these changes and how they worsen or improve over time.
Technology that can facilitate the automated assessment of speech can provide more objective measures to complement clinical rating scales. Winterlight’s approach involves automatically extracting hundreds of acoustic and linguistic properties of natural speech. The fine-grained data can be used to characterize the speech patterns associated with AD, FTD, and other neurodegenerative disorders. The platform uses short-recorded speech samples captured through speech tasks including picture description, paragraph reading, paragraph recall, phonemic fluency, semantic fluency, and object naming.
These advancements are helping to build a more complete picture of the common aspects of speech and language that decline with the progression of certain dementias. In analyzing just one to five minutes of a patient’s speech, hundreds of linguistic cues can be identified that may offer more accurate insight into the severity of their condition. These cues include lexical diversity, syntactic complexity, semantic content, and acoustics. Integrating speech-based technology into the design of AD and FTD clinical trials has the potential to contribute detailed insights into the subtle ways that patients’ symptoms improve or deteriorate as a study progresses.
From the patient’s perspective, testing via these types of platforms may also bring considerable improvements and reduce burden. As with all clinical trials, keeping AD and FTD patients, and their caregivers, engaged with a study is paramount. Typically, assessments are administered by study sites via tablets. A trained rater would walk the participant through a brief speech assessment, taking under five minutes to complete. The assessment would prompt them to speak in response to simple instructions; this may be a request to describe a picture, or simply asking a patient to express how they are feeling that day. The patient’s speech is recorded via the device’s microphone, with recordings uploaded to a cloud-based platform. The speech recordings are analyzed using Winterlight’s speech analysis platform and outcome measures are provided to the trial sponsor as an exploratory endpoint.
Assessments can also be made available to patients through their own tablets or smartphones, as well as devices provisioned by sponsors, for remote collection. Removing the need for patients to visit a site for this type of testing makes it easier for insights to be collected more frequently than may have been possible using traditional methods. Winterlight’s technology is currently being used in several Phase II and III clinical trials for AD and FTD, with many patients reporting that the assessments are easy-to-use and less taxing than other clinical assessments they have experienced. This work is helping to build greater understanding of the speech changes that occur in these patients over time.
There is tremendous potential for neurological language assessments to improve the way that clinical trials are run, and the impact of new treatments measured. As command of speech and language are ecologically and functionally relevant clinical markers of disease progression, objective tools can help generate novel, sensitive endpoints able to be administered remotely and at lower burden to the patient. While speech-based biomarkers are currently being used as exploratory endpoints, there is increased awareness of the enhancements they may bring to the accurate and sensitive measurement of cognitive health in AD and FTD research. As more evidence-based data and validation is gained, it is anticipated that these measures will eventually be adopted as primary and secondary endpoints and may provide more detailed insight into the subtle ways that new treatments are impacting cognitive health.
Jessica Robin is the Director of Clinical Research at Winterlight Labs