The era of precision neuroscience

A scientist examines brain scans on a large screen

Dr Steve Gardner, CEO of the computational biology company PrecisionLife, explores the challenges of achieving precision medicine in complex CNS conditions and explains how new precision neuroscience approaches are benefitting pharmaceutical companies who are again investing in the field to develop better treatments for neurological and neuropsychiatric diseases.

Neurological and neuropsychiatric disorders, which include Alzheimer’s disease, ALS, Parkinson’s disease, schizophrenia, depression, and anxiety disorders, are a leading cause of disability and fatality worldwide, accounting for 17% of global deaths and 10% of disability-adjusted life-years (GBD 2015 Neurological Disorders Collaborator Group, 2017). Their socioeconomic costs are measured in hundreds of billions of dollars per year.

The prevalence of these diseases has grown substantially in recent decades due to an increasing global population, longer life expectancies and increasing morbidity with predisposing risk factors. This now represents one of the most challenging and costly burdens to health and social care systems and societies worldwide.

The development of effective new disease-modifying treatments in neurodegenerative and neuropsychiatric disorders has been hindered by their inherent genetic complexity, environmental influences, and clinical variability. Precision approaches to neurological diseases, such as the use of tofersen to treat patients with specific SOD-1 mutations in ALS, are in their infancy but are fundamental to making meaningful progress and creating effective new therapeutic options in diseases such as ALS, Alzheimer’s, and schizophrenia, which all still have huge unmet medical need.

To find new ways of diagnosing and treating complex diseases we first must understand the mechanisms underpinning their key pathological drivers, how these relate to different patient subgroups, and which drugs might be useful in ameliorating their effects – this is the basis of precision neuroscience.

Guiding neurological drug discovery and development to success

The 20 years since the Human Genome Project has seen transformational advances in the molecular understanding of cancers and rare genetic diseases, leading to genetically informed, personalised selection of therapies and massively improved outcomes for many patients.

This is comparatively easier to accomplish in these diseases because they are relatively monogenic. Mutations in a gene may cause structural changes in its protein product, which can lead to a loss of function and consequently to the onset of a disease. While this is an oversimplification, often patients who share a specific mutation in their tumour can be treated in a similar fashion as they will share this common mechanistic aetiology for their disease, and this is relatively easy to detect using modern sequencing and clinical genomics tools.

Precision neuroscience has similar potential to revolutionise neurological drug development, but it requires a different way of thinking about disease than we usually apply in oncology. With complex psychiatric and neurodegenerative diseases there may be multiple ways to arrive at the same clinical diagnosis – patients may have fundamentally different causes of disease, but these end up presenting clinically with similar symptoms, so they get the same diagnosis.

Before we can effectively study these diseases, we therefore require a deep understanding of the mechanistic stratification of patients suffering from them. To do otherwise risks missing novel targets or overfocusing on the most obvious GWAS (Genome Wide Association Studies) associations, such as that linking the APOε4 gene to Alzheimer’s, in the expectation that we can force a ‘one-drug-fits-all’ solution. The fact that 100+ clinical trials in Alzheimer’s have failed to demonstrate clinical efficacy should warn us that this is too simplistic an approach in neurological diseases.

This is wholly unsurprising. If a disease has more than one mechanistic aetiology, it’s clear that the maximum efficacy that any drug will ever be able to demonstrate is equivalent to the prevalence of that disease mechanism in the patients recruited to a clinical trial. This is an obvious, accepted principle in oncology, and yet we keep expecting much more complex neurological diseases to behave in a much simpler ‘monogenic’ fashion. This has been the real failure of some areas of CNS drug discovery.

In reality, this is as much of an opportunity as it is a failure. It’s not accurate to infer that all of the drugs tested in these 100+ clinical trials did not work – in reality many of them probably did work (and there are certainly anecdotal reports of patient benefit for several), but just not in sufficient patients to demonstrate the level of clinical efficacy required or overcome side effect liabilities when they were tested across the whole cohort.

A precise understanding of neuroscience disease biology

Neurological diseases have been particularly challenging to treat due to their complexity; they are multifactorial in nature, arising from interactions between multiple genes and other clinical, epidemiological, and environmental factors, with significant individual variability in the underlying genetics and biology. They also involve multiple mutations in non-coding regions of DNA – the ‘dark genome’ which makes up around 98% of our DNA. Mutations in the dark genome affect gene regulation rather than coding, exerting a positive or negative impact on the expression of genes that may be related to disease processes. Due to this underlying complexity, and despite major research efforts, effective preventative and disease-modifying treatments are yet to materialise for diseases such as ALS, Alzheimer’s and Parkinson’s.

Central Nervous System (CNS) treatments in general have higher failure rates than non-CNS drugs, most often because of a lack of significant evidence of clinical efficacy and/or an unacceptable ratio of serious adverse events to demonstrable patient benefit. The development and post-development regulatory review times for CNS drugs can be significantly longer due to their complexity, although regulators such as the FDA are beginning to take a more flexible approach through limited/conditional approvals.

Precision neuroscience approaches need to integrate genomics, clinical, phenotype, and epidemiological data with human biology to better understand the etiologies of these complex diseases and mechanistically stratify patient subgroups. Only by stratifying CNS disorders more precisely can we identify novel druggable targets and distinguish the patient subgroups who will benefit from them using mechanistic biomarkers.

Mechanistic patient stratification

The complexity of neurological and neuropsychiatric diseases means that patients with the same diagnosis may well have different mechanistic etiologies and will benefit from different therapies based on their personal make-up and circumstances. Therefore, achieving precision neuroscience requires us to deploy mechanism-based stratification of patients within a disease population.

Unfortunately, these mechanistic differences don’t show up in the current gold-standard genomic analyses such as GWAS studies. Because it looks to find the independent effects of single SNPs, GWAS misses much of the disease signal in polygenic diseases, making it difficult to stratify patients. GWAS assumes the effects of SNPs on a disease are independent, additive, and the same across all patients, ignoring subgroups and interactions between SNPs and genes. It focuses on rarer variants, which are often not shared between different populations, especially those with different genetic ancestries.

To overcome this challenge, PrecisionLife has reimagined how to analyse multi-modal patient datasets and capture the non-linear interactions between multiple genes and exogenous factors in complex diseases. This combinatorial analytics approach reveals many more of the biological drivers of complex diseases than other methods like GWAS. Combinatorial analytics allows us to routinely stratify patient populations into clinically relevant subgroups based on their distinct mechanistic etiologies.

As an example, our research team analysed genomic data from 900 patients diagnosed with early-onset Alzheimer’s disease against healthy controls with no family history of the disease. While a traditional GWAS analysis of the same dataset identified only the APOε4 locus, our combinatorial analysis platform identified 267 SNPs that are highly associated with the risk of developing Alzheimer’s disease.

By clustering patients who in this case shared the same disease-associated SNPs, we found six major subgroups of patients with distinct mechanisms underpinning their disease.

Lipoprotein metabolism (the target for most current Alzheimer’s disease drug development programs) is the main driver of disease in this population, but crucially it is only relevant to 32% of these patients. This defines the upper bounds of clinical efficacy that is likely to be demonstrable in a Phase III trial if the patients are chosen at random, without mechanistic stratification, because they have a clinical diagnosis of Alzheimer’s and meet other non-genetic inclusion criteria.

Mechanistic stratification changes our view of the disease from being a single diagnostic label into a series of subgroups, each of which has a different disease etiology, each of which will therefore benefit from a different drug targeting a different mechanism of action.

With this level of insight:

  • we find new subgroups of patients each with a different mechanistic etiology with >15% prevalence, with multiple novel targets with strong genetic linkage for each patient subgroup,
  • we generate mechanistic patient stratification biomarkers which can be used as inclusion criteria for recruitment of participants into the clinical trial to enable the design of smaller, faster, and more targeted trials that are more likely to read out successfully,
  • complementary diagnostics can be developed based on those same patient stratification biomarkers – allowing clinicians to identify the right therapy for a specific patient and to support the launch and prescription of a new medication.

How precision neuroscience is benefiting pharmaceutical companies

Even stratified, these subgroups are still huge markets – Alzheimer’s and other dementias are estimated to directly cost US health systems $345B per year and this will rise by 2050 to $1 trillion. The socioeconomic costs are even higher. Even 15% of the Alzheimer’s market is worth >$5B/year on a willingness to pay basis.

All this is of huge interest when you’re putting a new drug on the market and working inside an outcomes-based remuneration model. However, for those pharmaceutical companies developing CNS treatments without having these patient stratification biomarkers to identify the clinical trial recruits who will benefit from the chosen mechanism, the upper limit of clinical efficacy that they can demonstrate is greatly compromised, and their programs are more likely to fail.

As well as condemning hundreds of millions of dollars’ worth of R&D investment to be spent in vain and billions of dollars of forecast revenues to be unrealised, this also means that CNS drugs that could in fact have been highly effective for many patients will not make it to market.

A mechanistic understanding of disease biology improves target selection, target validation, and patient stratification to increase R&D efficiency.

At PrecisionLife, our approach has already led to the identification of novel drug targets and precision repurposing opportunities for ALS, Alzheimer’s disease, Parkinson’s disease, schizophrenia, migraine, and depression. This has been achieved by mechanistically stratifying patient populations, like with the Alzheimer’s example above, uncovering subgroups with distinct disease etiologies, and identifying causal genes for those patients whose pathogenic protein products can be targeted.

These novel targets and repurposing opportunities are now being licensed to pharmaceutical companies to develop CNS programs with decreased development risk, accelerate regulatory approvals, and enhance market adoption.

Our precision neuroscience R&D partnerships with companies like Ono Pharmaceutical are based on understanding the specific target product profile (TPP) and disease indications that a partner wishes to address. We then identify novel targets which have the potential to meet this TPP, have good clinical prevalence, druggability and ideally also potential for use across multiple indications.

By using our mechanistic patient stratification biomarkers to recruit patients that are more likely to respond to a particular treatment, researchers can enrich clinical trials based on a clear mechanistic hypothesis. This approach can significantly increase the statistical power of clinical studies, meaning more efficient trial design and improving the likelihood of demonstrating clinical benefit to regulators -– reducing the cost of drug development, and accelerating the time to market for new treatments.

An example of this is our clinical development partnerships with clinical-stage pharmaceutical companies like Nanopharmaceutics. In this scenario, our patient stratification biomarkers are used to enrich multiple clinical trials, linking the development compounds’ mechanisms of action to the patient subgroups with the highest likelihood to respond, optimising patient selection and enabling smaller trials that readout faster.

Patient stratification biomarkers can also help to revive clinical programs that previously failed to demonstrate sufficient clinical efficacy. By retrospectively analysing our partners’ Phase III clinical trial data we’re able to generate biomarkers of drug response to identify patients that will respond to a particular treatment. Tooled with this insight, drug developers can revisit previously failed clinical programs and stalled assets to refine the design of their studies with inclusion/exclusion criteria to enrich trials with patients from the subgroup that will benefit from the treatment.

Once treatments gain market approval, this approach to precision neuroscience can help to improve the early and accurate diagnosis of neurological and neuropsychiatric diseases in the clinic. By identifying early biomarkers of disease, we can provide clinicians with the tools to intervene early, potentially slowing or even preventing disease progression. Using the insights from mechanistic patient stratification biomarkers, we’re actively enhancing clinical solutions with combinatorial risk scores and clinical decision support tools to predict, diagnose, and treat disease in complex, heterogeneous diseases more accurately.

These same biomarkers can also be used as complementary diagnostic tools to enable fast and effective launch of new drugs and support a desired price point in outcomes-based remuneration models by building clinician confidence and demonstrating high response rates from the first prescriptions.

This new precision neuroscience approach will be essential to overcome the fundamental challenges of such complex, heterogenous, and multi-factorial diseases, enabling a new era of patient-centred drug discovery and personalised healthcare in areas of significant unmet medical need.

DDW Volume 24 – Issue 4, Fall 2023 – Neuroscience Guide

About the author:

Steve GardnerDr Steve Gardner, is CEO of PrecisionLife. He is a serial technology entrepreneur with over 30 years’ experience developing and commercialising data science and informatics in the healthcare, life sciences and agri-food sectors. He is chair of the UK Bioindustry Association’s Genomic Advisory Committee, a former Global Director of Research Informatics for Astra A/B and has consulted with drug discovery and safety teams in over 20 biopharma companies.

Related Articles

Join FREE today and become a member
of Drug Discovery World

Membership includes:

  • Full access to the website including free and gated premium content in news, articles, business, regulatory, cancer research, intelligence and more.
  • Unlimited App access: current and archived digital issues of DDW magazine with search functionality, special in App only content and links to the latest industry news and information.
  • Weekly e-newsletter, a round-up of the most interesting and pertinent industry news and developments.
  • Whitepapers, eBooks and information from trusted third parties.
Join For Free