Todd Verdoorn

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2. Sources: cdc.gov – Cancer Data and Statistics, alz.org Alzheimer’s Data and Statistics, Nature.com Incidence of Parkinson disease in North America, National Library of Medicine – Stroke Epidemiology: Advancing Our Understanding of Disease Mechanism and Therapy, National Library of Medicine – The Epidemiology of Frontotemporal Dementia, Pubmed – The epidemiology of amyotrophic lateral sclerosis, Pubmed – The incidence and prevalence of Huntington’s disease: a systematic review and meta-analysis, Pubmed – Epidemiology of multiple system atrophy. ESGAP Consortium. European Study Group on Atypical Parkinsonisms, cdc.gov – Data and Statistics on Tourette Syndrome, Medscape – Charcot-Marie-Tooth Disease, Pubmed – Incidence of Dravet Syndrome in a US Population, NORD – Dravet Syndrome, National Library of Medicine – Lennox Gastaut Syndrome

NIH Grant Funding Across CNS Diseases

What guides NIH in distributing grants funds across diseases? A mix of medical need, scientific opportunity, and potential societal benefit are likely the primary drivers behind those decisions. However, I wouldn’t be surprised to see influences from patient advocate groups and federal politics creeping into the ultimate distribution of grant funds. We employed the IQuant engine to investigate this issue more deeply, beginning with sheer patient numbers as a rough surrogate for “medical need”. We analyzed the relationship between disease-specific funding levels over the past 5 years and estimates of US disease prevalence from 2022. We looked at a selected set of CNS diseases and compared them to cancer, writ large.

Notes

1. Average annual funding of extramural grants over the past 5 years.

At first glance, overall disease-focused grant funding tracked closely with the number of patients suffering from each disease. Note however, that across these diseases prevalence varies widely from around 17,000,000 for cancer to approximately 13,000 for multiple system atrophy. Things get interesting when looking at the NIH spend per patient across diseases. AD research attracts a respectable $213 per patient, well-aligned with the other “big” chronic diseases such as cancer ($140/patient) and Parkinson’s Disease ($262/patient). However, closer inspection of the per-patient spend in rarer diseases raises some questions. Amyotrophic Lateral Sclerosis research comes in at around $7000/patient and Multiple System Atrophy research receives nearly $3000/patient. Other severe, rare diseases such as Lennox Gastaut Syndrome are funded at surprisingly low rate of less than $10 per patient. It’s clear that overall numbers of patients are not a critical factor in distributing NIH grants. This current analysis did not include direct societal cost estimates (we’re working on that), but it is very difficult to argue that the societal cost of 7 million ischemic stroke patients is 200 times lower than that of 8600 ALS patients.

What else might underlie the differences? Of course, the scientific and medical opportunities in each field, while difficult to quantify, are likely quite variable. Again, it seems unlikely that research in Multiple System Atrophy is 10 times more compelling/rigorous/likely-to-succeed than research focused on Lennox Gastaut Syndrome. Another possibility is the relative number of grant applications coming into the system influences the number of funded applications. However, investigators follow the opportunities when proposing projects and causality between NIH funding patterns and numbers of successful grants clearly goes both ways. Notably our analysis, based on key-word searches, may not fully reflect NIH spending patterns and does not include spending by private organization (foundations and industry). Our search only included extramural grants, while NIH also invests in research through other means. We identified funded grants over 5 years based on searching grant titles and abstracts, which could, in theory, skew some results. For example, the differential diagnosis of Multiple System Atrophy and Parkinson’s Disease is challenging and is subject to significant study. Therefore, some of the grants assigned to Multiple System Atrophy may not be directly focused on that disease.

I would like to believe that scientific and clinical considerations are foremost, but it is clear other factors are at play.
– Dr. Todd Verdoorn

Despite these caveats, our analysis raises questions about how NIH distributes its investments across diseases. I would like to believe that scientific and clinical considerations are foremost, but it is clear other factors are at play. Perceived tractability or feasibility might be a prominent consideration, but a case could be made that more difficult areas require more investment, not less. I believe this is an important question and IQuant is actively researching how to incorporate scientific “quality” and “feasibility” into our analyses.

What about politics? Obviously, some patient groups could be more politically active (or politically connected) than others – leading to large funding differences across diseases. A more subtle form of politics could involve the social networks within each disease-specific research community. Could a more connected, interactive community lead to more NIH funding? IQuant continues to investigate measures of biomedical social network strength and influence. Stay tuned for more detailed analyses.

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NIH Grant Funding Across CNS Diseases

Notes

1. Average annual funding of extramural grants over the past 5 years.

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Observations from the trenches – due diligence

There have been many due diligence projects lately and the IQuant Engine has been working overtime to support them. As the funding environment gets more difficult, many programs, especially those that are in late discovery and early development, suddenly need financing and they are being shopped around with abandon. Here are a few reflections on what I’ve seen:

It seems alarmingly easy to generate positive drug treatment effects in rodent models of CNS disease. Some of this may be due to selection bias (there wouldn’t be a program to partner without demonstrated efficacy), but I can’t shake the worry that in vivo efficacy is not the stringent hurdle that it ought to be. If you’re looking to finance your new drug program, just realize that your efficacy data package is unlikely to be significantly more impressive than your competitor’s, and it won’t close the deal for you.

More rigor is needed in measuring drug potency at the postulated molecular target. I don’t care if your drug-target interaction is difficult to quantify, your program needs a reliable measure of absolute potency.

Pharmacokinetics (PK) is as important as rodent efficacy. PK includes drug plasma and brain concentrations after dosing as well as plasma and brain free fraction. Your efficacy results become substantially more believable if they are backed up by strong target engagement data. It is unbelievable how many drug programs don’t follow this fundamental precept.

The proportion of programs that avoid FDA interactions during early development is rising. It is easier and cheaper to conduct Phase 1 studies ex-US, especially in Australia. Too often this happens in programs that don’t have a sufficient data to pass FDA muster. Conducting Phase 1 studies in Australia because it’s cheaper may make some sense, but using Australia to avoid FDA feedback is a mistake.

Have a well-founded plan for establishing target engagement in early clinical trials. Preclinical data that validate target engagement biomarkers can help close a partnering deal. Not paying attention to this is an obvious gap that will only bring tears.

Neuroinflammation is the new apoptosis. Currently every disease involves neuroinflammation and every drug has anti-neuroinflammatory action. It’s not passe yet, but mechanistic fashions have a way of shifting out from under you.

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Observations from the trenches – due diligence

It seems alarmingly easy to generate positive drug treatment effects in rodent models of CNS disease. Some of this may be due to selection bias (there wouldn’t be a program to partner without demonstrated efficacy), but I can’t shake the worry that in vivo efficacy is not the stringent hurdle that it ought to be. If you’re looking to finance your new drug program, just realize that your efficacy data package is unlikely to be significantly more impressive than your competitor’s, and it won’t close the deal for you.
More rigor is needed in measuring drug potency at the postulated molecular target. I don’t care if your drug-target interaction is difficult to quantify, your program needs a reliable measure of absolute potency.
Pharmacokinetics (PK) is as important as rodent efficacy. PK includes drug plasma and brain concentrations after dosing as well as plasma and brain free fraction. Your efficacy results become substantially more believable if they are backed up by strong target engagement data. It is unbelievable how many drug programs don’t follow this fundamental precept.
The proportion of programs that avoid FDA interactions during early development is rising. It is easier and cheaper to conduct Phase 1 studies ex-US, especially in Australia. Too often this happens in programs that don’t have a sufficient data to pass FDA muster. Conducting Phase 1 studies in Australia because it’s cheaper may make some sense, but using Australia to avoid FDA feedback is a mistake.
Have a well-founded plan for establishing target engagement in early clinical trials. Preclinical data that validate target engagement biomarkers can help close a partnering deal. Not paying attention to this is an obvious gap that will only bring tears.
Neuroinflammation is the new apoptosis. Currently every disease involves neuroinflammation and every drug has anti-neuroinflammatory action. It’s not passe yet, but mechanistic fashions have a way of shifting out from under you.

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Revisiting An Early IQuant Post

Some may remember these two charts, which I posted on LinkedIn in 2016. They highlight the disconnect between the amount of NIH grant funding for and the publication productivity for selected neurological diseases. Not surprisingly, Alzheimer’s disease won the grant funding category, but this money generated significantly fewer publications per dollar compared to other disease areas such as epilepsy, Parkinson’s Disease, and multiple sclerosis.

Figure 1 – Publications and NIH grants, 2012 – 2015

Figure 2 – Publications generated per $1 million in grant funding

During the subsequent seven years, what have these investments yielded for patients in each of these disease areas?

Disease	Clinical Trials¹	Publications²	Novel Treatments³
Multiple Sclerosis	1218	33,700	6
Parkinson’s	1565	55,400	4⁴
Epilepsy	715	45,800	4
Alzheimer’s	1286	80,000`	2

Notes
1. All clinical trials, (ClinicalTrials.gov)
2. All publications (Pubmed)
3. List of Annual Innovative Drug Approvals (US FDA)
4. Includes the approval of Deep Brain Stimulation

Of course, grant funding generates a variety of patient benefits, but let’s face it, new treatments are clearly the most visible and impactful.

The controversial approvals of both highly similar AD drugs raised many questions. The clinical benefit seems modest at best, each has notable side effects, and they command a premium price. Moreover, the nearly $1 billion of annual NIH grant support is only a small fraction of the total research investment in AD, which includes an uncomfortably large number of very expensive, failed clinical trials. Why has so much money has been funneled into AD research for such limited benefit? Clearly the heavy focus on a single disease mechanism, A-beta/amyloid likely squeezed out more attractive alternatives and impeded overall progress. It is certainly worth exploring, as we must learn to avoid these traps. In any case, a look into the status and prospects of alternative mechanisms is warranted. Stay tuned for more on this from Iquant.

Figure 1 – Publications and NIH grants, 2012 – 2015

Figure 2 – Publications generated per $1 million in grant funding

During the subsequent seven years, what have these investments yielded for patients in each of these disease areas?

Disease	Clinical Trials¹	Publications²	Novel Treatments³
Multiple Sclerosis	1218	33,700	6
Parkinson’s	1565	55,400	4⁴
Epilepsy	715	45,800	4
Alzheimer’s	1286	80,000`	2

Notes
1. All clinical trials, (ClinicalTrials.gov)
2. All publications (Pubmed)
3. List of Annual Innovative Drug Approvals (US FDA)
4. Includes the approval of Deep Brain Stimulation

Of course, grant funding generates a variety of patient benefits, but let’s face it, new treatments are clearly the most visible and impactful.

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Revisiting An Early IQuant Post

Figure 1 – Publications and NIH grants, 2012 – 2015

Figure 2 – Publications generated per $1 million in grant funding

During the subsequent seven years, what have these investments yielded for patients in each of these disease areas?

Table 1 – Research productivity since 2016

Disease	Clinical Trials¹	Publications²	Novel Treatments³
Multiple Sclerosis	1218	33,700	6
Parkinson’s	1565	55,400	4⁴
Epilepsy	715	45,800	4
Alzheimer’s	1286	80,000	2

Notes
1. All clinical trials, (ClinicalTrials.gov)
2. All publications (Pubmed)
3. List of Annual Innovative Drug Approvals (US FDA)
4. Includes the approval of Deep Brain Stimulation

Of course, grant funding generates a variety of patient benefits, but let’s face it, new treatments are clearly the most visible and impactful.

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Why IQuant?

As a scientist who has been discovering and developing CNS drugs for the past 25 years, I’ve worked on a wide variety of diverse projects. Although each project involved unique mechanisms and novel indications, they shared a common requirement: a foundational understanding of the scientific literature, patents, funding landscape, and clinical trial activity. As if this wasn’t daunting enough, it is equally critical for scientists to understand and engage with the network of experts within each topic area. Who is doing the best work? Who could be a strong collaborator? Who shows leadership and innovation?

Even though answering these questions accurately is a time-consuming and resource intensive process, it is fundamentally necessary for progress in any project. This is a monumental task, as even within the narrowest of research areas the vast number of papers and pace of change is nearly overwhelming. Staying current is often severely compromised by the sad fact that much of the scientific literature is either unreliable, irrelevant, or downright fraudulent. After decades in the field, I knew there must be a more effective and efficient way to integrate useful knowledge without devoting limited free time to poring through endless documents, most of questionable relevance.

In contrast to my initial academic career, where the topic area was well-defined and stable over time, the shift to drug discovery and development required a more applied research sensibility together with greater intellectual agility. Shifting project priorities, new disease areas, and unplanned opportunities all required the tools to pivot rapidly and quickly gain the necessary scientific background. Each change called for detailed intelligence gleaned from voluminous, but often flawed publications. I found myself seeking new, more efficient ways to embrace each such challenge. Basic searches on Pubmed or Medline proved counterproductive, largely due to the sheer weight of information to digest. The more advanced databases such as those offered by big publishers (you probably have sales e-mails from them sitting in your spam folders right now) demanded expensive subscriptions and yet were only marginally better. Contracting with vendors to create white papers or topic dossiers was equally frustrating, each at a price point approaching six figures(!) and requiring at least six months and too much of my time. After being thoroughly disappointed by the lack of acceptable options, my partner Benjamin and I began a collaboration to address this need.

Intuitive Quantitation’s founding principles and capabilities arose directly from these experiences. With a fundamental belief in unlocking the incredible value buried in publicly available data, we worked together to create the Iquant Engine. Iquant was built to rapidly find the best collaborators for preclinical work or Key Opinion Leaders for clinical programs. We provide the most critical background information to support due diligence, identify strengths, catch flaws, and uncover hidden opportunities. Iquant does this through comprehensive searches of public data, unique analytics that integrate metadata, decisive interpretation, and fully documented methods. Our products provide quick, detailed results that never require subscriptions – the insights, analysis, and data provided belong to you.

As a company we will continue to conduct research and develop related technologies for integrating data, communicating results, and facilitating progress through knowledge. We are committed to sharing our results on a regular basis, either here or in peer-reviewed, open-source journals. Check back regularly for new posts discussing the capabilities and uses of the Iquant Engine. Please use our contact form if you have any questions or are interested in working together on your next project.

Our Services

Let us know how we can help enhance your research.

Why IQuant?

This is a monumental task, as even within the narrowest of research areas the vast number of papers and pace of change is nearly overwhelming. Staying current is often severely compromised by the sad fact that much of the scientific literature is either unreliable, irrelevant, or downright fraudulent. After decades in the field, I knew there must be a more effective and efficient way to integrate useful knowledge without devoting limited free time to poring through endless documents, most of questionable relevance.

Basic searches on Pubmed or Medline proved counterproductive, largely due to the sheer weight of information to digest. The more advanced databases such as those offered by big publishers (you probably have sales e-mails from them sitting in your spam folders right now) demanded expensive subscriptions and yet were only marginally better. Contracting with vendors to create white papers or topic dossiers was equally frustrating, each at a price point approaching six figures(!) and requiring at least six months and too much of my time. After being thoroughly disappointed by the lack of acceptable options, my partner Benjamin and I began a collaboration to address this need.

Our Services

Let us know how we can help enhance your research.