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Challenges and Strategies to designing and managing successful Longevity Clinical Trials: A data science perspective

April 13, 2022

-by Raghav Sehgal, Scientific Venture consultant and Jyothi Devakumar, Chief Scientific Officer

Aging is the greatest risk factor for most diseases. Interventions that slow the progression of aging per se, could delay the onset of age-related diseases and death collectively, rather than one at a time. This is the premise underlying the geroscience hypothesis. The geroscience hypothesis is supported by recent, remarkable progress in understanding the basic biology of aging as well as identifying interventions that can extend healthy lifespan.  

Geroscience research has advanced unifying mechanisms and theories to explain the broad biological aging process and to identify pathways that can be targeted to delay or reverse age-related decline. Biological aging has been posited to be pharmacologically targetable, and several promising candidates have been identified. 

For example, there is a growing body of evidence that metformin can delay aging (with the TAME trial underway to prove this hypothesis). Though lifespan effects are relatively modest (∼4–6% extension of median lifespan across mouse breeds), the effects on health are substantial, with improvements on tests of physical and cognitive function, cataracts, oral glucose, and insulin tolerance improved by up to 30%.These preclinical studies provide a proof-of-concept that therapeutically targeting fundamental mechanisms of aging can improve healthspan and lifespan. The challenge comes in translation, as large-scale clinical trials designed to target aging have never before been attempted.

Challenges in longevity clinical trials

Determining the effect of interventions on delaying or alleviating aging processes in humans, in our opinion, will ultimately require one or more randomized, controlled clinical trials conducted over a sufficient time period in a large, heterogeneous older adult population testing “hard” outcomes such as burden of chronic diseases, functional dependence, and/or mortality. 

The first step on a path to large clinical trials is a series of smaller clinical trials that can provide evidence for the concept that aging processes can be intervened in humans, provide biological data for reverse translation studies of the effects of interventions targeting basic aging mechanisms, suggest or validate intermediate outcomes such as biomarkers or surrogate clinical endpoints, and inform the design and scale of larger trials. 

Challenges encountered in randomized clinical trials are well described and include protocol adherence by study participants or study sites; dropouts leading to missing data; result interpretation of the “intention to treat” analysis, where everyone in the treatment group is analyzed together regardless of their adherence to the study intervention; participant diversity (gender, ethnicity, geography, and age); and generalizability. 

Beyond these challenges, longevity clinical trials face a whole additional set of hurdles. In the following sections, we will discuss some of the issues and challenges unique to the design and analysis of longevity clinical trials that test interventions targeting fundamental aging mechanisms.

Population selection

Rather in our opinion, inclusion criteria in longevity clinical trials would ideally be as broad as possible within the context of the trial design. This is very different from the present approach of most disease specific trials which have very strong exclusion criteria and especially for patients with multiple comorbidities. Rather, the practice of excluding very elderly people or patients with extensive comorbidities would be counterproductive, because these people are those who would be most likely to benefit from interventions targeting basic aging mechanisms and for whom the interventions may ultimately be used.  That being said, in many circumstances, it may make sense to stratify by frailty, system function (Cardiovascular, Hepatic and more), comorbidities, or biomarkers upon enrollment and preferentially target those patients at higher risk for the primary outcome to increase power and the likelihood of benefit.  

A key consideration in selecting the study population is the degree of risk the population has for primary outcomes such as age-related morbidity and disability. The study design would thus favor selection of older individuals (eg, age ≥ 65 years) who already manifest at least one age-related disease or condition, who are therefore at increased risk for age-related morbidity and disability. Study participants could also manifest conditions that are considered risk factors for disease or disability, such as metabolic syndrome, mild cognitive impairment, or subclinical vascular disease or diseases that are not yet associated with significant dysfunction, such as hypertension and early stages of chronic kidney disease (CKD Stages 1 and 2). Thus, individuals living independently in the community who do not have significant disease burden, but have some manifestations of aging, would be the population of choice for a drug intervention trial. This selection strategy would also facilitate individuals’ participation in the study because they ideally would travel to study visits. Studies should also exclude individuals with limited life expectancy (<5 years), who would be unlikely to complete the relatively long follow-up period of such trials.

All in all, population selection for longevity trials needs to be broad and more inclusive than being exclusive, which is quite the shift in the present paradigm of how clinical trials are designed, thus would require a completely new perspective and tools to design such trials. 

Intervention specific Trial Design

Interventions for longevity could broadly be categorized into 3 groups: therapeutics, supplements and lifestyle changes. For most present day therapeutics of any kind the long standing rule are rigorous clinical trials. For supplements and lifestyle interventions often more observational trials are built which are not as rigorous as their therapeutic counterparts. We believe that more rigorous trials are needed for not just therapeutics but supplements as well which is not the norm while lifestyle interventions can still follow the present day norm of observational trials. 

Within therapeutics there are three classes of drugs- repurposed, novel therapeutic agents and combination therapies. Selecting a drug that is already in clinical use and that demonstrates a good safety profile with pleiotropic effects on multiple systems may be ideal.  Alternatively, a new drug specifically developed to target the known pathways implicated in aging, such as mTOR-inhibiting rapalogs or senescent-cell-clearing senolytics could be considered. 

Despite the disadvantages associated with novel therapeutics, such as the need for pharmacokinetic data in older individuals, drug safety information, and the lack of knowledge about its effects on age-related dysfunction and diseases in humans, there is an impending need to develop these. 

Combination therapies are increasingly becoming one of the hot topics as the importance of pan-hallmark based interventions is recognized such as combination of senolytics with rejuvenation strategies. Combinations of drugs acting on complementary pathways may maximize benefits and minimize side effects, and efficacy of drugs might be potentiated by simultaneous nondrug interventions (eg, exercise).  

For example, combining metformin with rapamycin may not only provide additional effects on fundamental aging processes but may also alleviate the insulin resistance and hyperglycemia related to rapamycin. A randomized, double-blind placebo controlled trial is the gold standard. The placebo in this case would likely include an inactive pill, but the current standard of care will also need to be incorporated into all study arms. In a health-span study, diet and exercise counseling might be offered to all participants. Existing geriatric-focused care programs both in and out of the hospital might provide an ideal framework for conducting these trials. Investigators should be comfortable with multifactorial interventions and pragmatic study design to reflect the current standard of care in geriatrics and the nature of the population being targeted.

This intervention specific trial design is another indication for the need of novel methods and tools for longevity clinical trial design, management and data analysis.

Measurable outcomes

The main outcome of interest for a longevity clinical trial would be whether an intervention can target fundamental aging processes as measured by age-related diseases and functional disability.  Therefore, disease-free survival or disability-free survival would be the primary outcomes of interest in clinical trials of agents that target fundamental aging processes.  

In a longitudinal study of health span, this might be operationalized as time to incidence of a second or third age-related disease or impairment of a first activity of daily living. Particularly for larger studies, the community of older adults can be involved in determining high-priority outcomes—perhaps, for example, independence in activities of daily living (ADLs) or avoiding institutionalization. Such outreach could help build broader public support, enhance recruitment, assist in advocacy for funding, and ensure the relevance of the studies to the general population. 

Recording clinical events like new onset of disease requires careful and systematic adjudication. After an event is reported, it must be reviewed and confirmed by experienced physicians. This involves obtaining the participants’ medical records, after receiving their informed consent, from medical institutions where the event was documented and/or treated. Adjudication of events is usually based on a set of prespecified criteria and is performed by a designated committee that is blinded to the intervention in order to ensure objectivity.

In certain situations, primary outcomes of clinical trials can be surrogate endpoint biomarkers that are highly predictive of an actual clinical event, such as high blood sugar, which is predictive of diabetes mellitus, or hypertension and increased circulating lipids, which are predictive of CVD. Such biomarkers can be used as endpoints for trials, but extensive testing and validation are required before they are accepted by the medical community and regulators in place of actual clinical events. 

At present, there are no validated, easily measurable biomarkers that predict age-related morbidity and disability in large populations. Potential biomarker candidates that have been linked to function and health span in older adults in limited studies include interleukin-6, IGF-1, and IGF binding proteins. Development of reliable biomarkers or surrogate markers could dramatically accelerate aging research and should be one goal of initial pilot studies. 

Clearly, data collection as well as novel measures of outcomes are going to be an additional challenge to the design and implementation of longevity trials which would require development of novel data science methodologies and tools to tackle some of these challenges.

From preclinical aging animal models to human trials

Imagine a world with a cure for all chronic conditions from Alzheimer’s, cancer, and diabetes to aging. Well, that world exists if you’re a laboratory mouse. When partaking in longevity-promoting experimentation, even some worms have lived a lifetime and a half longer than their non-tampered counterparts. Unfortunately for humans, a good chunk of these anti-aging and lifespan-prolonging approaches that work on animals don’t seem to translate, probably due to evolutionary reasons. The move from slowing fundamental processes of aging in laboratory animals to slowing aging in humans will not be as simple as prescribing a pill and watching it work. Only after a therapeutic is confirmed as safe and effective in a preclinical model like laboratory mice or human cells can it be tested in people. Yet, most therapeutics don’t even make the hurdle of being safe in humans, let alone effective. 

One of the most famous examples is a clinical study conducted for an antibody called TGN1412 that preclinically demonstrated its therapeutic potential in autoimmune diseases, such as rheumatoid arthritis.  In the case of TGN1412, at least part of the problem was that the drug`s target, a protein on specific immune cells differs slightly between the monkey and human versions.  Specific immune cells in mice lacked a receptor present in humans that strongly binds TGN1412, causing over-activation of the cells and the failure to predict a lethal `cytokine storm,` where the immune system goes haywire, in humans.

The take-home message: though we may have the same genetic makeup, it doesn't mean that the genes are activated in the same cells or play the same role in aging animals and humans.  Individual differences in the causes of peoples` aging will complicate protein production from DNA by expanding alternative gene copies that mutes the effects of treatments, thus requiring extensive samples for aging science clinical trials.  

A human adult's pace of biological aging may be sped or slowed by genetics, which is far more complex than inbred lab mice, which are all essentially clones of one another.  The early-life experiences and exposures and the differences in several behavioral and social lifestyle factors that do not characterize laboratory animals, like diet, physical activity, sleep, mental health, and smoking, have significant effects on healthspan and lifespan.  Human-relevant factors like these have not been studied in animal models of aging.  However, much human observational research shows that early-life behavioral and social risk factors can statistically predict strict aging outcomes, such as the timing of late-life disease onset and early mortality.  There’s much more to be considered, including indicators of social status in childhood and adulthood, race and ethnicity, adverse experiences in childhood, adult trauma, negative psychological states, poor health behaviors, and age and gender. 

Translation of pre-clinical successes to clinical successes has always been a challenge for drug discovery but more so in aging where appropriate animal aging models are lacking or absent. This gap could potentially be filled by newer computational models and in silico drug interaction simulation models specific to humans that can replicate the human biology as closely as they can. 

From challenges to strategies using AI interventions

Clinical trial design, management and data analysis is undergoing a data revolution. With the advent of integration of large scale Omic data sets ranging from Clinical-omics (EHR data) down to single cell omics, the data deluge has both been a boon and bane. This data is only helpful in improving in the design and management of clinical trials if we are able to appropriately analyze and interpret it. Clearly, the bottleneck is no longer the data generation but rather the data processing and insight generation from this data. This has given way to a boom in development of computational tools, platforms and methods to solve these challenges which are now being integrated into the daily working of these trials. 

In a way, longevity clinical trials are the next generation of trials since a lot of these novel AI tools to solve challenges in this space would already be at the disposal of researchers thereby allowing them to really leverage these tools to their max. That being said, specific clinical trial AI solutions need to be built for the specific challenges of longevity trials. Take for example the challenge around patient selection, AI based EHR data screening algorithms to identify a biologically diverse yet representative population could be a potential solution to solving the population selection challenge while also maintaining the constraints and exclusion criteria required for specific interventions. Similarly, novel clinical trial design tools that can simulate trial outcomes in different scenarios that prevent any oversight in the design of complex longevity trials could be significantly helpful. Clinical trial data management platforms that automate the process of recording and checking of data integrity would be critical to capturing outcomes over the extended length of longevity clinical trials. On that note, AI based biomarker discovery can significantly enable the discovery of novel proxy outcomes that can significantly decrease the time required to run longevity clinical trials. On the same lines, in the pre-clinical settings, better computational and drug interaction simulation models that replicate the aged human biology can be significantly critical to test new longevity therapies, thereby decreasing our dependence on the more unpredictable animal models.