There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Angelia Wang |
| Institution: | Janssen R&D |
| Department: | Clinical Pharmacology and Pharmacometrics |
| Country: | |
| Proposed Analysis: | My colleagues and I are responsible for the model-informed drug development (MIDD) of two investigational compounds currently being developed to treat Alzheimer’s Disease (AD). One major goal of this modeling effort is to predict efficacy of these treatments as monotherapy, or in combination with other successful AD therapies. This necessitates quantitating the relationship between Alzheimer’s-specific biomarkers and clinical symptoms. Our specific clinical endpoint of interest is iADRS, a linear composite of the two more commonly used endpoints Adas-Cog and iADL. Although iADRS is considered to be more predictive than either its subcomponents, there are currently far more studies correlating key biomarkers such as Aβ and tau with Adas-Cog, and those studies often report adjusted measurements (e.g. z-scores normalized to cognitively normal brains). We would instead like access the raw, unnormalized data instead in order to enable more empirical assessments and thus develop a robust disease progression model. We intend to use this data to establish longitudinal relationships that relate the dynamic range of both Aβ (soluble oligomers, protofibrils, and plaques), tau (CSF t-tau, CSF p-tau, and NFTs) to cognitive scores over time. It is anticipated that our modeling assessments, critically informed by the ADNI data, will further our mechanistic and quantitative understanding of target modulation and changes in cognitive improvement in AD, consequently streamlining the organization’s R&D efforts. |
| Additional Investigators | |
| Investigator's Name: | Vivaswath Ayyar |
| Proposed Analysis: | My colleagues and I are responsible for the model-informed drug development (MIDD) of two investigational compounds currently being developed to treat Alzheimer’s Disease (AD). One major goal of this modeling effort is to predict efficacy of these treatments as monotherapy, or in combination with other successful AD therapies. This necessitates quantitating the relationship between Alzheimer’s-specific biomarkers and clinical symptoms. Our specific clinical endpoint of interest is iADRS, a linear composite of the two more commonly used endpoints Adas-Cog and iADL. Although iADRS is considered to be more predictive than either its subcomponents, there are currently far more studies correlating key biomarkers such as Aβ and tau with Adas-Cog, and those studies often report adjusted measurements (e.g. z-scores normalized to cognitively normal brains). We would instead like access the raw, unnormalized data instead in order to enable more empirical assessments and thus develop a robust disease progression model. We intend to use this data to establish longitudinal relationships that relate the dynamic range of both Aβ (soluble oligomers, protofibrils, and plaques), tau (CSF t-tau, CSF p-tau, and NFTs) to cognitive scores over time. It is anticipated that our modeling assessments, critically informed by the ADNI data, will further our mechanistic and quantitative understanding of target modulation and changes in cognitive improvement in AD, consequently streamlining the organization’s R&D efforts. |
| Investigator's Name: | Weirong Wang |
| Proposed Analysis: | My colleagues and I are responsible for the model-informed drug development (MIDD) of two investigational compounds currently being developed to treat Alzheimer’s Disease (AD). One major goal of this modeling effort is to predict efficacy of these treatments as monotherapy, or in combination with other successful AD therapies. This necessitates quantitating the relationship between Alzheimer’s-specific biomarkers and clinical symptoms. Our specific clinical endpoint of interest is iADRS, a linear composite of the two more commonly used endpoints Adas-Cog and iADL. Although iADRS is considered to be more predictive than either its subcomponents, there are currently far more studies correlating key biomarkers such as Aβ and tau with Adas-Cog, and those studies often report adjusted measurements (e.g. z-scores normalized to cognitively normal brains). We would instead like access the raw, unnormalized data instead in order to enable more empirical assessments and thus develop a robust disease progression model. We intend to use this data to establish longitudinal relationships that relate the dynamic range of both Aβ (soluble oligomers, protofibrils, and plaques), tau (CSF t-tau, CSF p-tau, and NFTs) to cognitive scores over time. It is anticipated that our modeling assessments, critically informed by the ADNI data, will further our mechanistic and quantitative understanding of target modulation and changes in cognitive improvement in AD, consequently streamlining the organization’s R&D efforts. |
| Investigator's Name: | Vrishali Salian |
| Proposed Analysis: | My colleagues and I are responsible for the model-informed drug development (MIDD) of two investigational compounds currently being developed to treat Alzheimer’s Disease (AD). One major goal of this modeling effort is to predict efficacy of these treatments as monotherapy, or in combination with other successful AD therapies. This necessitates quantitating the relationship between Alzheimer’s-specific biomarkers and clinical symptoms. Our specific clinical endpoint of interest is iADRS, a linear composite of the two more commonly used endpoints Adas-Cog and iADL. Although iADRS is considered to be more predictive than either its subcomponents, there are currently far more studies correlating key biomarkers such as Aβ and tau with Adas-Cog, and those studies often report adjusted measurements (e.g. z-scores normalized to cognitively normal brains). We would instead like access the raw, unnormalized data instead in order to enable more empirical assessments and thus develop a robust disease progression model. We intend to use this data to establish longitudinal relationships that relate the dynamic range of both Aβ (soluble oligomers, protofibrils, and plaques), tau (CSF t-tau, CSF p-tau, and NFTs) to cognitive scores over time. It is anticipated that our modeling assessments, critically informed by the ADNI data, will further our mechanistic and quantitative understanding of target modulation and changes in cognitive improvement in AD, consequently streamlining the organization’s R&D efforts. |
