Alzheimer’s research has had some cruel experiences in the last few years. Drugs that seemed so promising with animal models failed when tested on humans. These frustrating outcomes caused the National Institute of Aging to dig deeper to determine why animal models were not translating to human success.
One of the Institute’s projects was AlzPED (Alzheimer’s Disease Preclinical Efficacy Database), not just a database of research papers, but “curated” papers evaluated as to whether the paper includes the factors listed below. As the mission statement states: AlzPED is designed to help identify the critical data, design elements and methodology missing from studies; making them susceptible to misinterpretation, less likely to be reproduced and reducing their translational value.
The categories and comments were added by me, so should be taken with a grain of salt. For the category called “Stats”, keep in mind the importance of statistics to experiment design. The gold standard of experiment design is a “randomized double blind” model. Neither the patient nor the researcher knows whether the patient is receiving the experimental treatment, a standard treatment or a placebo. Patients are assigned randomly to a category. How the researcher assures a random selection and sufficient sample size would be defined in the statistical plan. “Subjects” category refers to test subjects, and “Drug” category refers to the drug or other therapy being tested.
As you scan this list of factors, you can start to see how this level of detail would facilitate translating the animal experiment to a human experiment. How many research papers describe all these factors? Very few. AlzPED’s 2020 analysis of 1,030 papers indicated that only 2% included power/sample size calculation while 95% or greater included drug parameters such as dose, frequency, transport. Of the nine core experimental design elements, a mere 0.2% of reports had all nine. Few research papers reported more than 5 core design elements, and most studies reported only 2-4 core design elements.
So, still much training and development of templates is needed to improve Alzheimer’s research papers so they are more likely to translate from mouse success to human success. These factors look like they are not Alzheimer’s-specific, and could also be used to evaluate Parkinson’s research papers.
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|Conflict of Interest||General||Y||Financial conflicts of researcher (e.g., receives grants from drug company)|
|Power/Sample Size Calculation||Stats||Y||How many test subjects needed to conclude with, e.g., 95% confidence (power)|
|Blinded for Treatment||Stats||Y||Chosen “blind” – without knowing if in Control group|
|Blinded for Outcome Measures||Stats||Y||Chosen “blind” – without knowing if in Control group|
|Randomized into Groups||Stats||Y||Random selection of Control Group vs Therapy Group|
|Inclusion/Exclusion Criteria Included||Subjects||Y||Example: Subjects must be 18-80 and diagnosis < 5 years|
|Study Balanced for Sex||Subjects||Y||# of males vs # of females|
|Sex as a Biological Variable||Subjects||Y|
|Number of Excluded Animals||Subjects|
|Number of Premature Deaths||Subjects|
|Age at the Beginning of Treatment||Subjects|
|Age at the End of Treatment||Subjects|
|ADME Measures||Drug||Absorption, Distribution, Metabolism and Excretion. ADME studies are designed to investigate how a chemical (e.g. a drug compound) is processed by a living organism|
|Biomarkers||Drug||What biological measures (e.g., blood, skin, cerebral fluid) are used|
|Pharmacodynamic Measures||Drug||Measures of what the drug does to the body|
|Pharmacokinetic Measures||Drug||Measures of what the body does to the drug|
|Toxicology Measures||Drug||Lethal dose and other measures of how toxic the drug is on test subjects|
|Formulation||Drug||Composition of drug|
|Frequency of Administration||Drug|
|Duration of Treatment||Drug|
|Route of Delivery||Drug||How drug is delivered (e.g., orally, intravenously)|