Alzheimer's disease beyond amyloid: Can the repetitive failures of amyloid-targeted therapeutics inform future approaches to dementia drug discovery?

Alzheimer's Disease (AD) therapeutics based on the amyloid hypothesis have repeatedly failed in clinical trials. Together with numerous reports that amyloid is present in brains from aged individuals without cognitive dysfunction, this suggests that the association of amyloid with AD is collateral rather than causal. However, the preeminence of the amyloid hypothesis has resulted in the 'systematic …thwart[ing of] alternative approaches' to AD/dementia driven by a 'cabal' of amyloid acolytes who have effectively controlled the ideas funded and published, which startups received venture investment and which programs were advanced in biopharmaceutical companies where they consulted. As a result, dementia research is estimated to be 15-30 years behind where it could be with conflicting data ignored in favor of the amyloid dogma and clinical trial failures being ascribed to faulty design or inadequacies in the compound selection process including flawed animal models. Major concerns regarding the precise diagnosis of AD/dementia and conflicting views on the validated status of fluid biomarker assays have resulted in trials that included patients with unknown amyloid pathologies. With the failure of the amyloid approach, emerging data on the role(s) of vascular, mitochondrial and synaptic network dysfunction, infection, diabetes, sleep, hearing loss, the gut microbiome and neuroinflammation/ innate immune function as dementia targets are driving research in new directions bolstered by recent findings on the genetic, omics and systems biology associated with AD/dementia. In moving forward, lessons learnt from the amyloid debacle should be used to enhance the objective identification of AD/dementia therapeutics as a multifactorial disease syndrome.

The de-Alzheimerization of age-related dementias: implications for drug targets and approaches to effective therapeutics.

Alzheimer's disease (AD) was differentiated from senile dementia (SD) in 1910 due to its early onset and pathological severity. In 1976, this distinction was upended when SD was redesignated as AD to focus efforts and funding in dementia-related research. AD then became conflated with amyloid plaques and, to a lesser degree, neurofibrillary tangles complicating efforts in understanding dementia causality and its treatment. The resultant four-decade search for therapies-based almost exclusively on amyloid was an exercise in futility. While dementia is a complex, multifactorial syndrome, AD is viewed as a homogeneous, linear disease. An amyloid-agnostic approach is necessary to discover therapeutics for age-related dementias.

Preclinical Models of Alzheimer's Disease: Relevance and Translational Validity.

The only drugs currently approved for the treatment of Alzheimer's Disease (AD) are four acetylcholinesterase inhibitors and the NMDA antagonist memantine. Apart from these drugs, which have minimal to no clinical benefit, the 40-year search for effective therapeutics to treat AD has resulted in a clinical failure rate of 100% not only for compounds that prevent brain amyloid deposition or remove existing amyloid plaques but also those acting by a variety of other putative disease-associated mechanisms. This indicates that the preclinical data generated from current AD targets to support the selection, optimization, and translation of new chemical entities (NCEs) and biologics to clinical trials is seriously compromised. While many of these failures reflect flawed hypotheses or a lack of adequate characterization of the preclinical pharmacodynamic and pharmacokinetic (PD/PK) properties of lead NCEs-including their bioavailability and toxicity-the conceptualization, validation, and interrogation of the current animal models of AD represent key limitations. The overwhelming majority of these AD models are transgenic, based on aspects of the amyloid hypothesis and the genetics of the familial form of the disease. As a result, these generally lack construct and predictive validity for the sporadic form of the human disease. The 170 or so transgenic models, perhaps the largest number ever focused on a single disease, use rodents, mainly mice, and in addition to amyloid also address aspects of tau causality with more complex multigene models including other presumed causative factors together with amyloid. This overview discusses the current animal models of AD in the context of both the controversies surrounding the causative role of amyloid in the disease and the need to develop validated models of cognitive function/dysfunction that more appropriately reflect the phenotype(s) of human aged-related dementias. © 2019 by John Wiley & Sons, Inc.

Alzheimer's disease (AD) therapeutics - 2: Beyond amyloid - Re-defining AD and its causality to discover effective therapeutics.

Compounds targeted for the treatment of Alzheimer's Disease (AD) have consistently failed in clinical trials despite evidence for target engagement and pharmacodynamic activity. This questions the relevance of compounds acting at current AD drug targets - the majority of which reflect the seminal amyloid and, to a far lesser extent, tau hypotheses - and limitations in understanding AD causality as distinct from general dementia. The preeminence of amyloid and tau led to many alternative approaches to AD therapeutics being ignored or underfunded to the extent that their causal versus contributory role in AD remains unknown. These include: neuronal network dysfunction; cerebrovascular disease; chronic, local or systemic inflammation involving the innate immune system; infectious agents including herpes virus and prion proteins; neurotoxic protein accumulation associated with sleep deprivation, circadian rhythm and glymphatic/meningeal lymphatic system and blood-brain-barrier dysfunction; metabolic related diseases including diabetes, obesity hypertension and hypocholesterolemia; mitochondrial dysfunction and environmental factors. As AD has become increasingly recognized as a multifactorial syndrome, a single treatment paradigm is unlikely to work in all patients. However, the biomarkers required to diagnose patients and parse them into mechanism/disease-based sub-groups remain rudimentary and unvalidated as do non-amyloid, non-tau translational animal models. The social and economic impact of AD is also discussed in the context of new FDA regulatory draft guidance and a proposed biomarker-based Framework (re)-defining AD and its stages as part of the larger landscape of treating dementia via the 2013 G8 initiative to identify a disease-modifying therapy for dementia/AD by 2025.

Alzheimer's disease (AD) therapeutics - 1: Repeated clinical failures continue to question the amyloid hypothesis of AD and the current understanding of AD causality.

Deposits of amyloid plaques and neurofibrillary tangles of aggregated tau in the brain represent key hallmarks of the neurodegenerative disorder, Alzheimer's Disease (AD) and form the basis of the major hypotheses of AD causality. To date, therapeutics that reduce brain amyloid in AD patients have demonstrated no effect in reversing the associated decline in cognition or function indicating that the amyloid hypothesis is either incorrect or that there is a point when the disease becomes independent of Aß production or is refractory to any type of therapeutic intervention. The clinical failures of inhibitors of tau aggregation, neurotransmitter modulators and drugs repurposed from AD-associated disease indications tend to support this latter viewpoint. Current understanding of AD causality is thus incomplete, a situation that has been compounded by a debate on whether AD is a singularly distinct form of dementia and by the dogmatic promotion of hypotheses over actual clinical data. The latter has repeatedly led to compounds lacking efficacy in Phase II trials being advanced into Phase III where their lack of efficacy is routinely recapitulated. This Commentary, the first of two, discusses amyloid and tau as putative drug targets for AD in the context of the prevalence and economic and social impact of this insidious neurodegenerative disease.

Manipulating cell fate while confronting reproducibility concerns.

Biomedical research is being transformed by the discovery and use of human pluripotent stem cells (hPSCs). Remarkable progress has been made, and assorted clinical trials are underway related to the application of stem cell therapy, including transplantation of hPSC-derived cells, in situ reprogramming or transdifferentiation, and utilization of targets and compounds identified from patient-derived stem cells. However, the pace of discovery is overwhelming efforts to replicate the work of others, prompting a concern over validity and reproducibility. Here, we address some sources of variability in reprogramming, maintaining, and differentiating hPSCs that impact interpretation of studies involving their use, and how it relates to efforts to move the field forward. The commitment in time and resources required to generate and maintain cell-lines, coupled with marked variations between hPSCs derived from patients with the same disease, has resulted in a fundamental change in how research is conducted. Dr. Michael Williams has championed the need to appropriately validate all cell-lines before use to limit sources of variability, although defining what constitutes a validated hPSC in the era of single cell-omics can be challenging.

Enhancing reproducibility: Failures from Reproducibility Initiatives underline core challenges.

Efforts to address reproducibility concerns in biomedical research include: initiatives to improve journal publication standards and peer review; increased attention to publishing methodological details that enable experiments to be reconstructed; guidelines on standards for study design, implementation, analysis and execution; meta-analyses of multiple studies within a field to synthesize a common conclusion and; the formation of consortia to adopt uniform protocols and internally reproduce data. Another approach to addressing reproducibility are Reproducibility Initiatives (RIs), well-intended, high-profile, systematically peer-vetted initiatives that are intended to replace the traditional process of scientific self-correction. Outcomes from the RIs reported to date have questioned the usefulness of this approach, particularly when the RI outcome differs from other independent self-correction studies that have reproduced the original finding. As a failed RI attempt is a single outcome distinct from the original study, it cannot provide any definitive conclusions necessitating additional studies that the RI approach has neither the ability nor intent of conducting making it a questionable replacement for self-correction. A failed RI attempt also has the potential to damage the reputation of the author of the original finding. Reproduction is frequently confused with replication, an issue that is more than semantic with the former denoting "similarity" and the latter an "exact copy" - an impossible outcome in research because of known and unknown technical, environmental and motivational differences between the original and reproduction studies. To date, the RI framework has negatively impacted efforts to improve reproducibility, confounding attempts to determine whether a research finding is real.

The academic-industrial complex: navigating the translational and cultural divide.

In general, the fruits of academic discoveries can only be realized through joint efforts with industry. However, the poor reproducibility of much academic research has damaged credibility and jeopardized translational efforts that could benefit patients. Meanwhile, journals are rife with articles bemoaning the limited productivity and increasing costs of the biopharmaceutical industry and its resultant predilection for mergers and reorganizations while decreasing internal research efforts. The ensuing disarray and uncertainty has created tremendous opportunities for academia and industry to form even closer ties, and to embrace new operational and financial models to their joint benefit. This review article offers a personal perspective on the opportunities, models and approaches that harness the increased interface and growing interdependency between biomedical research institutes, the biopharmaceutical industry and the technological world.

Unknown unknowns in biomedical research: does an inability to deal with ambiguity contribute to issues of irreproducibility?

The credibility and consequent sustainability of the biomedical research "ecosystem" is in jeopardy, in part due to an inability to reproduce data from the peer-reviewed literature. Despite obvious and relatively inexpensive solutions to improve reproducibility-ensuring that experimental reagents, specifically cancer cell lines and antibodies, are authenticated/validated before use and that best practices in statistical usage are incorporated into the design, analysis, and reporting of experiments-these are routinely ignored, a reflection of hubris and a comfort with the status quo on the part of many investigators. New guidelines for the peer review of publications and grant applications introduced in the past year, while well-intended, lack the necessary consequences, e.g., denial of funding, that would result in sustained improvements when scientific rigor is lacking and/or transparency is, at best, opaque. An additional factor contributing to irreproducibility is a reductionist mindset that prioritizes certainty in research outcomes over the ambiguity intrinsic to biological systems that is often reflected in "unknown unknowns". This has resulted in a tendency towards codifying "rules" that can provide "yes-no" outcomes that represent a poor substitute for the intellectual challenge and skepticism that leads to an awareness and consideration of "unknown unknowns". When acknowledged as potential causes of unexpected experimental outcomes, these can often transition into the "knowns" that facilitate positive, disruptive innovation in biomedical research like the human microbiome. Changes in investigator mindset, both in terms of validating reagents and embracing ambiguity, are necessary to aid in reducing issues with reproducibility.

Guidelines for manuscript submission in the peer-reviewed pharmacological literature.

Recent reports have highlighted studies in biomedical research that cannot be reproduced, tending to undermine the credibility, relevance and sustainability of the research process. To address this issue, a number of factors can be monitored to improve the overall probability of reproducibility. These include: (i) shortcomings in experimental design and execution that involve hypothesis conceptualization, statistical analysis, and data reporting; (ii) investigator bias and error; (iii) validation of reagents including cells and antibodies; and (iv) fraud. Historically, research data that have undergone peer review and are subsequently published are then subject to independent replication via the process of self-correction. This often leads to refutation of the original findings and retraction of the paper by which time considerable resources have been wasted in follow-on studies. New NIH guidelines focused on experimental conduct and manuscript submission are being widely adopted in the peer-reviewed literature. These, in their various iterations, are intended to improve the transparency and accuracy of data reporting via the use of checklists that are often accompanied by "best practice" guidelines that aid in validating the methodologies and reagents used in data generation. The present Editorial provides background and context to a newly developed checklist for submissions to Biochemical Pharmacology that is intended to be clear, logical, useful and unambiguous in assisting authors in preparing manuscripts and in facilitating the peer review process. While currently optional, development of this checklist based on user feedback will result in it being mandatory within the next 12 months.

Replicated, replicable and relevant-target engagement and pharmacological experimentation in the 21st century.

A pharmacological experiment is typically conducted to: i) test or expand a hypothesis regarding the potential role of a target in the mechanism(s) underlying a disease state using an existing drug or tool compound in normal and/or diseased tissue or animals; or ii) characterize and optimize a new chemical entity (NCE) targeted to modulate a specific disease-associated target to restore homeostasis as a potential drug candidate. Hypothesis testing necessitates an intellectually rigorous, null hypothesis approach that is distinct from a high throughput fishing expedition in search of a hypothesis. In conducting an experiment, the protocol should be transparently defined along with its powering, design, appropriate statistical analysis and consideration of the anticipated outcome (s) before it is initiated. Compound-target interactions often involve the direct study of phenotype(s) unique to the target at the cell, tissue or animal/human level. However, in vivo studies are often compromised by a lack of sufficient information on the compound pharmacokinetics necessary to ensure target engagement and also by the context-free analysis of ubiquitous cellular signaling pathways downstream from the target. The use of single tool compounds/drugs at one concentration in engineered cell lines frequently results in reductionistic data that have no physiologically relevance. This overview, focused on trends in the peer-reviewed literature, discusses the execution and reporting of experiments and the criteria recommended for the physiologically-relevant assessment of target engagement to identify viable new drug targets and facilitate the advancement of translational studies.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Translational paradigms in pharmacology and drug discovery.

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

Animal models of asthma: reprise or reboot?

Animal models of disease represent the pinnacle of hierarchical research efforts to validate targets and compounds for therapeutic intervention. Yet models of asthma, particularly in the mouse, which, for practical reasons, has become the sine qua non of asthma research, have been a bone of contention for decades. With barely a nod to their limitations and an extensive history of translational failures, they continue to be used for target identification and to justify the clinical evaluation of new compounds. Recent improvements - including sensitization directly to the airways; use of more relevant allergens; development of a chronic rather than short-term condition; utilization of techniques to measure lung function beyond uninterpretable measures of airway hyperresponsiveness - are laudable but cannot bridge the chasm between the models and the myriad complexities of the human disorder and multiple asthma endophenotypes. While further model developments are necessary, including recognition of key environmental factors beyond allergens, the judicious integration with newer ex vivo and in vitro techniques, including human precision-cut lung slices, reprograming of patient-derived induced pluripotent stem cells and fibroblasts to epithelial and smooth muscle cells, and use of other clinical samples to create a more holistic depiction of activities, might improve their translational success.

Alzheimer's therapeutics: continued clinical failures question the validity of the amyloid hypothesis-but what lies beyond?

The worldwide incidence of Alzheimer's disease (AD) is increasing with estimates that 115 million individuals will have AD by 2050, creating an unsustainable healthcare challenge due to a lack of effective treatment options highlighted by multiple clinical failures of agents designed to reduce the brain amyloid burden considered synonymous with the disease. The amyloid hypothesis that has been the overarching focus of AD research efforts for more than two decades has been questioned in terms of its causality but has not been unequivocally disproven despite multiple clinical failures, This is due to issues related to the quality of compounds advanced to late stage clinical trials and the lack of validated biomarkers that allow the recruitment of AD patients into trials before they are at a sufficiently advanced stage in the disease where therapeutic intervention is deemed futile. Pursuit of a linear, reductionistic amyloidocentric approach to AD research, which some have compared to a religious faith, has resulted in other, equally plausible but as yet unvalidated AD hypotheses being underfunded leading to a disastrous roadblock in the search for urgently needed AD therapeutics. Genetic evidence supporting amyloid causality in AD is reviewed in the context of the clinical failures, and progress in tau-based and alternative approaches to AD, where an evolving modus operandi in biomedical research fosters excessive optimism and a preoccupation with unproven, and often ephemeral, biomarker/genome-based approaches that override transparency, objectivity and data-driven decision making, resulting in low probability environments where data are subordinate to self propagating hypotheses.

Translational semantics and infrastructure: another search for the emperor's new clothes?

The successful transition of drug-like new chemical entities from discovery to clinical trials coupled with real-time feedback from the latter represents a key element for success in drug discovery. Now designated as T1 translational medicine, this process has, similar to other recent solutions to improve productivity, been hyped as a novel discipline, despite the fact that many of its component activities have existed in the pharmacological sciences for many decades. Instead of proselytizing translational medicine, the priority is to improve the quality of the science and decision-making processes involved in advancing compounds to ensure that what is translated has value.

The increasing challenge of discovering asthma drugs.

The prevalence of asthma continues to rise. Current drugs provide symptomatic relief to some, but not all, patients. Despite the need for new therapeutics, and a huge research effort, only four novel agents from two classes of drugs - the antileukotrienes and an anti-IgE antibody - have been approved in the last 30 years. This review highlights three particular issues that contribute to the challenge of identifying new therapeutics. First is an over-reliance on animal models of allergy to define targets and expectations of efficacy that has met with poor translation to the clinical setting. While sensitivity to particular aeroallergens is one key risk factor for asthma, atopy and asthma are not synonymous, and while about half of adult asthmatics are atopic the incidence of allergic asthma is probably <50%. The second issue is a fundamental disconnect between the directions of basic research and clinical research. Basic research has developed a detailed, reductive, unifying mechanism of antigen-induced, T helper type 2 cell-mediated airway inflammation as the root cause of asthma. In contrast, clinical research has started to identify multiple asthma phenotypes with differing cellular components, mediators and sensitivities to asthma drugs, and probably varying underlying factors including susceptibility genes. Finally, different features of asthma - bronchoconstriction, symptoms, and exacerbations - respond diversely to treatment; effects that are not captured in animal models which do not develop asthma per se, but utilize unvalidated surrogate markers. Basic research needs to better integrate and utilize the clinical research findings to improve its relevance to drug discovery efforts.

Asthma translational medicine: report card.

Over the last 30 years, scientific research into asthma has focused almost exclusively on one component of the disorder - airway inflammation - as being the key underlying feature. These studies have provided a remarkably detailed and comprehensive picture of the events following antigen challenge that lead to an influx of T cells and eosinophils in the airways. Indeed, in basic research, even the term "asthma" has become synonymous with a T helper 2 cell-mediated disorder. From this cascade of cellular activation processes and mediators that have been identified it has been possible to pinpoint critical junctures for therapeutic intervention, leading experimentalists to produce therapies that are very effective in decreasing airway inflammation in animal models. Many of these compounds have now completed early Phase 2 "proof-of-concept" clinical trials so the translational success of the basic research model can be evaluated. This commentary discusses clinical results from 39 compounds and biologics acting at 23 different targets, and while 6 of these drugs can be regarded as a qualified success, none benefit the bulk of asthma sufferers. Despite this disappointing rate of success, the same immune paradigm and basic research models, with a few embellishments to incorporate newly identified cells and mediators, continue to drive target identification and drug discovery efforts. It is time to re-evaluate the focus of these efforts.