When assessing the efficacy of a drug for neurological diseases, such as Alzheimer’s disease and Parkinson’s disease, pharmaceutical companies face several challenges with subject recruitment and the ability to get drugs to market. These challenges often result from a lack of reliable, accessible, and affordable tools for screening, subject selection, and longitudinal monitoring.
Below, we look into common clinical trial challenges, measurement scales and scoring systems for neurocognitive assessments and their limitations, and how clinical trials for neurological diseases can be streamlined.
Subject recruitment for clinical trials for neurological diseases can be extremely costly. Take Alzheimer’s disease, for example—to determine subject eligibility, many Alzheimer’s clinical trials require positron emission (PET) scans as a part of the key inclusion criteria to establish protein levels, such as beta-amyloid and tau. As there can be hundreds of potential candidates, this process can cost hundreds of thousands of dollars. Historically, there has been a lack of reliable measurement tools available to narrow down the subject pool before completing hundreds of costly PET scans.
PET imaging looks for reduced beta-amyloid protein levels in the brain and is frequently utilized as a primary outcome to determine drug efficacy. However, protein levels in the brain may not be the best indicator of whether or not an Alzheimer’s subject has improved because it isn’t clear if the development and progression of beta-amyloid plaques correlate with neurocognitive decline. Neither brain imaging nor traditional neurocognitive assessments can determine the impact of the drug on the subject’s ability to function and complete Activities of Daily Living (ADLs).
Measurement scales and scoring systems for neurocognitive assessments, such as the Mini-Mental State Exam (MMSE) and Montreal Cognitive Assessment (MoCA), are quite limited in providing meaningful measures of neurocognitive abilities. The MMSE and MoCA are both scored on a single 30-point scale, meaning they only provide a single macro-level score and low granularity analog data.
Perhaps the biggest limitations of measurement scales and scoring systems for neurocognitive assessments are their narrowness and lack of ecological validity, or the concept of realism with which the design of neurocognitive evaluation matches and aligns with neurocognitive states required to complete ADLs.
Other limiting factors of measurement scales and scoring systems for neurocognitive assessments include the following:
The ability to simulate complex ADLs and collect highly granular data measuring a wide range of neurocognitive abilities across many domains can streamline clinical trial recruitment as well as act as a novel metric throughout clinical trials to assess drug efficacy.
The simulation of ADLs can be used as an assessment during subject recruitment to make an initial eligibility determination for the large population of potential candidates, thus narrowing the eligible subject pool. From here, the subset of subjects can receive a PET scan to confirm candidate eligibility, saving thousands of dollars on PET scans.
Altoida is building the world’s first Precision Neurology platform and app-based medical device to provide a more comprehensive and ecologically valid method for assessing neurocognitive function in the most accurate, effective, and cost-efficient way possible.
By completing a series of augmented reality and motor activities designed to simulate complex ADLs on a smartphone or tablet, Altoida’s device provides new and robust measurements of neurocognitive function across 13 unique neurocognitive domains:
Our device measures and analyzes nearly 800 cognitive and functional digital biomarkers proven to be clinically significant through over 20 years of rigorous scientific research. Through the collection of highly granular data from integrated smartphone or tablet sensors, Altoida’s device produces comprehensive neurocognitive domain scores.
Along with our innovative artificial intelligence, this method will pioneer fully digital predictive neurological disease diagnosis. After our recent Breakthrough Device designation by the FDA, Altoida’s technology will provide individuals with a predictive score that will enable a highly accurate prediction of whether an individual aged 55 and older will or will not convert from Mild Cognitive Impairment to Alzheimer’s disease within 12 months.
To learn more about Altoida’s Precision Neurology platform or measurement scales and scoring systems for neurocognitive assessments, contact us today.