Incentives for Starting Small Companies Focused on Rare and Neglected Diseases

Repurposing the Dihydropyridine Calcium Channel Inhibitor Nicardipine as a Nav1.8 Inhibitor In Vivo for Pitt Hopkins Syndrome

PURPOSE

Individuals with the rare genetic disorder Pitt Hopkins Syndrome (PTHS) do not have sufficient expression of the transcription factor 4 (TCF4) which is located on chromosome 18. TCF4 is a basic helix-loop-helix E protein that is critical for the normal development of the nervous system and the brain in humans. PTHS patients lacking sufficient TCF4 frequently display gastrointestinal issues, intellectual disability and breathing problems. PTHS patients also commonly do not speak and display distinctive facial features and seizures. Recent research has proposed that decreased TCF4 expression can lead to the increased translation of the sodium channel Na_v1.8. This in turn results in increased after-hyperpolarization as well as altered firing properties. We have recently identified through a drug repurposing screen an FDA approved dihydropyridine calcium antagonist nicardipine used to treat angina, which inhibited Na_v1.8.

METHODS

We have now performed behavioral testing in groups of 10 male Tcf4(± ) PTHS mice dosing by oral gavage at 3 mg/kg once a day for 3 weeks using standard methods to assess sociability, nesting, fear conditioning, self-grooming, open field and test of force.

RESULTS

Nicardipine returned this spectrum of behavioral deficits in the Tcf4(± ) PTHS mouse model to WT levels and resulted in statistically significant results.

CONCLUSIONS

These in vivo results in the well characterized Tcf4(± ) PTHS mice may suggest the potential to test this already approved drug further in a clinical study with PTHS patients or suggest the potential for use off label under compassionate use with their physician.

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.

Raising awareness of the importance of funding for tuberculosis small-molecule research

Tuberculosis (TB) drug discovery research is hampered by several factors, but as in many research areas, the available funding is insufficient to support the needs of research and development. Recent years have seen various large collaborative efforts involving public-private partnerships, mimicking the situation during the golden age of antibiotic drug discovery during the 1950s and 1960s. The large-scale collaborative efforts funded by the European Union (EU) are now subject to diminishing financial support. As a result, TB researchers are increasingly looking for novel forms of funding, such as crowdfunding, to fill this gap. Any potential solution will require a careful reassessment of the incentives to encourage additional organizations to provide funding.

Enabling Anyone to Translate Clinically Relevant Ideas to Therapies

How do we inspire new ideas that could lead to potential treatments for rare or neglected diseases, and allow for serendipity that could help to catalyze them? How many potentially good ideas are lost because they are never tested? What if those ideas could have lead to new therapeutic approaches and major healthcare advances? If a clinician or anyone for that matter, has a new idea they want to test to develop a molecule or therapeutic that they could translate to the clinic, how would they do it without a laboratory or funding? These are not idle theoretical questions but addressing them could have potentially huge economic implications for nations. If we fail to capture the diversity of ideas and test them we may also lose out on the next blockbuster treatments. Many of those involved in the process of ideation may be discouraged and simply not know where to go. We try to address these questions and describe how there are options to raising funding, how even small scale investments can foster preclinical or clinical translation, and how there are several approaches to outsourcing the experiments, whether to collaborators or commercial enterprises. While these are not new or far from complete solutions, they are first steps that can be taken by virtually anyone while we work on other solutions to build a more concrete structure for the "idea-hypothesis testing-proof of concept-translation-breakthrough pathway".

The Next Era: Deep Learning in Pharmaceutical Research

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Open drug discovery for the Zika virus

The Zika virus (ZIKV) outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.