Why have transgenic rodent models failed to successfully mimic Alzheimer’s disease. How can we develop effective drugs without them?

Evidence of disturbed insulin signaling in animal models of Alzheimer's disease

Since glucose reuptake by neurons is mostly independent of insulin, it has been an intriguing question whether insulin has or not any roles in the brain. Consequently, the identification of insulin receptors in the central nervous system has fueled investigations of insulin functions in the brain. It is also already known that insulin can influence glucose reuptake by neurons, mostly during activities that have the highest energy demand. The identification of high density of insulin receptors in the hippocampus also suggests that insulin may present important roles related to memory. In this context, studies have reported worse performance in cognitive tests among diabetic patients. In addition, alterations in the regulation of central insulin pathways have been observed in the brains of Alzheimer's disease (AD) patients. In fact, some authors have proposed AD as a third type of diabetes and recently, our group proposed insulin resistance as a common link between different AD hypotheses. Therefore, in the present narrative review, we intend to revise and gather the evidence of disturbed insulin signaling in experimental animal models of AD.

A patient advocating for transparent science in rare disease research

300 million people live with at least one of 6,000 rare diseases worldwide. However, rare disease research is not always reviewed with scrutiny, making it susceptible to what the author refers to as nontransparent science. Nontransparent science can obscure animal model flaws, misguide medicine regulators and drug developers, delay or frustrate orphan drug development, or waste limited resources for rare disease research. Flawed animal models not only lack pharmacologic relevance, but also give rise to issue of clinical translatability. Sadly, these consequences and risks are grossly overlooked. Nontransparency in science can take many forms, such as premature publication of animal models without clinically significant data, not providing corrections when flaws to the model are discovered, lack of warning of critical study limitations, missing critical control data, questionable data quality, surprising results without a sound explanation, failure to rule out potential factors which may affect study conclusions, lack of sufficient detail for others to replicate the study, dubious authorship and study accountability. Science has no boarders, neither does nontransparent science. Nontransparent science can happen irrespective of the researcher's senority, institutional affiliation or country. As a patient-turned researcher suffering from Bietti crystalline dystrophy (BCD), I use BCD as an example to analyze various forms of nontransparent science in rare disease research. This article analyzes three papers published by different research groups on Cyp4v3^-/-, high-fat diet (HFD)-Cyp4v3^-/-, and Exon1-Cyp4v3^-/- mouse models of BCD. As the discussion probes various forms of nontransparent science, the flaws of these knockout mouse models are uncovered. These mouse models do not mimic BCD in humans nor do they address the lack of Cyp4v3 (murine ortholog of human CYP4V2) expression in wild type (WT) mouse retina which is markedly different from CYP4V2 expression in human retina. Further, this article discusses the impact of nontransparent science on drug development which can lead to significant delays ultimately affecting the patients. Lessons from BCD research can be helpful to all those suffering from rare diseases. As a patient, I call for transparent science in rare disease research.

Potential therapeutic effects of boswellic acids/Boswellia serrata extract in the prevention and therapy of type 2 diabetes and Alzheimer's disease

The link between diabetes and cognitive dysfunction has been reported in many recent articles. There is currently no disease-modifying treatment available for cognitive impairment. Boswellia serrata (B. serrata) is used traditionally to treat chronic inflammatory diseases such as type 2 diabetes (T2D), insulin resistance (IR), and Alzheimer's disease (AD). This review aims to highlight current research on the potential use of boswellic acids (BAs)/B. serrata extract in T2D and AD. We reviewed the published information through June 2021. Studies have been collected through a search on online electronic databases (Academic libraries as PubMed, Scopus, Web of Science, and Egyptian Knowledge Bank). Accumulating evidence in preclinical and small human clinical studies has indicated that BAs/B. serrata extract has potential therapeutic effect in T2D and AD. According to most of the authors, the potential therapeutic effects of BAs/B. serrata extract in T2D and AD can be attributed to immunomodulatory, anti-inflammatory, antioxidant activity, and elimination of the senescent cells. BAs/B. serrata extract may act by inhibiting the IκB kinase/nuclear transcription factor-κB (IKK/NF-κB) signaling pathway and increasing the formation of selective anti-inflammatory LOX-isoform modulators. In conclusion, BAs/B. serrata extract may have positive therapeutic effects in prevention and therapy of T2D and AD. However, more randomized controlled trials with effective, large populations are needed to show a definitive conclusion about therapeutic efficacy of BAs/B. serrata extract in T2D and AD.

Insulin Resistance as a Common Link Between Current Alzheimer's Disease Hypotheses

Almost 115 years ago, Alois Alzheimer described Alzheimer's disease (AD) for the first time. Since then, many hypotheses have been proposed. However, AD remains a severe health public problem. The current medical approaches for AD are limited to symptomatic interventions and the complexity of this disease has led to a failure rate of approximately 99.6%in AD clinical trials. In fact, no new drug has been approved for AD treatment since 2003. These failures indicate that we are failing in mimicking this disease in experimental models. Although most studies have focused on the amyloid cascade hypothesis of AD, the literature has made clear that AD is rather a multifactorial disorder. Therefore, the persistence in a single theory has resulted in lost opportunities. In this review, we aim to present the striking points of the long scientific path followed since the description of the first AD case and the main AD hypotheses discussed over the last decades. We also propose insulin resistance as a common link between many other hypotheses.

Recent Advances in the Potential of Cannabinoids for Neuroprotection in Alzheimer's, Parkinson's, and Huntington's Diseases

Three prevalent neurodegenerative diseases, Parkinson's, Alzheimer's, and Huntington's are in need of symptomatic relief of slowing disease progression or both. This chapter focuses on the potential of cannabinoids to afford neuroprotection, i.e. avoid or retard neuronal death. The neuroprotective potential of cannabinoids is known from the work in animal models and is mediated by the two cannabinoid receptors (CB₁/CB₂) and eventually, by their heteromers, GPR55, orphan receptors (GPR3/GPR6/GPR12/GPR18), or PPARγ. Now, there is the time to translate the findings into patients. The chapter takes primarily into account advances since 2016 and addresses the issue of proving neuroprotection in humans. One recent discovery is the existence of activated microglia with neuroprotective phenotype; cannabinoids are good candidates to skew phenotype, especially via glial CB₂ receptors (CB₂R), whose targeting has, a priori, less side effects those targeting the CBs₁ receptor (CB₁R), which are expressed in both neurons and glia. The fact that a cannabis extract (SativexTM) is approved for human therapy, such that cannabis use will likely be legalized in many countries and different possibilities that cannabinoid pharmacology suggests a successful route of cannabinoids (natural or synthetic) all the way to be approved and used in the treatment of neurodegeneration.

Multivariate MR biomarkers better predict cognitive dysfunction in mouse models of Alzheimer's disease

To understand multifactorial conditions such as Alzheimer's disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between pathology affected brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD, while being raised in uniform environments and with known genotype alterations. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI traditional voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included hippocampus, olfactory areas, entorhinal cortex and cerebellum, as well as the frontal association area. The properties of these regions, extracted from each of the imaging markers, were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning, allowing the detection of associations between context, location, and responses. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, expected to play a role in memory dysfunction, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our cross-sectional results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.