QGATD

Traditional methods of drug discovery and repurposing are based on the concept of Structure-Activity Relationship (SAR) which is used to predict biological activity from molecular structure. 

The computational process employed to capture and quantify SAR is very complex. It involves enormous 3-dimensional simulations and extremely large chunks of information. This makes quantitative SAR (QSAR) methods time-consuming and very resource-intensive. 

Another flaw of the SAR/QSAR approach is the observed phenomenon whereby two molecules having identical structures do not elicit similar biological effects. There are also exceptional cases where two unidentical molecules are found to have similar biological effects. This phenomenon negatively affects the accuracy of SAR/QSAR as a predictive model for identifying candidate drugs for novel diseases.

Quantum Genomics Approach To Therapeutics Design, QGATD, solves these two pain points of traditional drug design and repurposing. It is both time and resource-effective and can be equitably scaled with ease.

On the 5th of January 2020, the World Health Organization, WHO, officially issued its first Disease Outbreak News report on the novel coronavirus disease. 5 days later, it confirmed receiving the genetic sequence for the novel coronavirus. On the 24th of January, WHO held an informal consultation on the prioritization of candidate therapeutic agents for use in the novel coronavirus infections. However, the choice of candidate drugs was not based on the new virus. Rather, it was based on the efficacy of existing broad-spectrum antivirals and treatments used for MERS-CoV, another coronavirus similar to the novel coronavirus. The WHO's excuse was that there were gaps in knowledge around the new virus, notwithstanding the availability of its genetic sequence. Another obvious reason would be that the response time required to significantly reduce the speed of disease transmission and spread would not allow for the traditional methods of drug repurposing. 

The case would have been different if a solution like my proprietary QGATD was applied to identify candidate therapeutic agents within minutes or hours of receiving the genetic sequence of SARS-CoV-2. Maybe, the disease outbreak in Wuhan, China would not have progressed into a global pandemic. 

The QGATD solution drastically reduces the time required to identify candidate drugs for trial prioritization in times of health emergency and guarantees a quick therapeutic response to new disease outbreaks.

Quantum Genomics Approach To Therapeutics Design, QGATD, is a web-enabled platform for analytics and automated computations related to therapeutics development and repurposing. It deploys genomic/proteomic data and a Machine Learning algorithm to determine specific quantum mechanical attributes (QMA) of therapeutic targets and corresponding therapeutic agents. The QMA data Is then used to screen and match drug-target pairs according to desired therapeutic effects; whether inhibitory or supportive.

How the QGATD solution works: 

1. A researcher or health professional studies the genetic sequence and identifies therapeutic targets from the genomic/proteomic data.  
2. The researcher copies the genomic/proteomic data, usually a protein sequence, and enters it in the input section of the QGATD portal.
3. The Machine Learning algorithm (MLA) of the QGATD analyzes the data and computes the quantum mechanical attributes, QMA, of the therapeutic target.
4. With the QMA data of the therapeutic target, the MLA then computes the QMA of the ideal corresponding therapeutic agent.
5. The QMA data of the therapeutic agent is then used as a benchmark to screen and match drugs in a pharmacological database connected via an Application Programming Interface (API).
6. Lastly, the matching drugs are outputted and ranked according to how likely they are to elicit the expected therapeutic effect.

The QGATD solution is a unique innovation. As such, its target audience includes the entire value chain of the global therapeutics development community. To mention a few:

• Research scientists
• Health professionals
• Pharmacologists 
• Drug administration regulators
• Disease control agencies
• Public health policymakers
• The World Health Organisation

Therefore, by extension, the QGATD solution serves the general public.

Specifically, the QGATD solution helps the target audience to circumvent the drawbacks of the traditional computational approach to drug repurposing as discussed in the preceding sections of this entry. It helps them to accurately identify candidate therapeutic agents (drugs) in record time, hence guaranteeing a quick therapeutic response to new disease outbreaks.

I would also point out here that I use "Therapeutics" in a broad sense to include pharmaceutical and nutraceutical drugs, herbal medicine, and other complementary/alternative modalities like aromatherapy and sound therapy.

The QGATD solution will create a tangible impact on global health, especially in health emergencies where a quick therapeutic response time is required. Let me use the circumstances surrounding the COVID-19 pandemic to illustrate how the QGATD will impact future health emergencies.

On the 5th of January 2020, the World Health Organization, WHO, officially issued its first Disease Outbreak News report on the novel coronavirus disease. 5 days later, it confirmed receiving the genetic sequence for the novel coronavirus. On the 24th of January, WHO held an informal consultation on the prioritization of candidate therapeutic agents for use in the novel coronavirus infections. However, the choice of candidate drugs was not based on the new coronavirus. Rather, it was based on the efficacy of existing broad-spectrum antivirals and treatments used for MERS-CoV, another coronavirus similar to the novel coronavirus. The WHO's excuse was that there were gaps in knowledge around the new virus. I believe there were more reasons for that seemingly counterintuitive decision. 

It was a race against time and the traditional model for identifying candidate drugs as of January 2020 was time-intensive. The time cost would have run into months if the WHO decided to identify candidate drugs specifically for the new virus. So, not only was there a knowledge gap, but there was also a solution gap.

With the QGATD solution, the story would have been different. The genetic sequence received on the 10th of January 2020 would have just been enough to select therapeutic targets and deploy the QGATD to identify candidate drugs to be prioritized for trials. This would have been achieved the same day the genetic sequence was received. Furthermore, the candidate drug identification would have been more accurate and the race to repurpose a drug for COVID-19 would have yielded a better result in record-breaking time. Such is the impact the QGATD solution is capable of creating if embraced and deployed during health emergencies.

In March of 2020, when I got hold of the full genomic sequence of SARS-CoV-2, I mined the proteomic data for therapeutic targets and deployed a manual prototype of QGATD to identify candidate therapeutic agents for COVID-19. The therapeutic targets I considered include the Spike protein, Replicase polyprotein 1ab, Nucleoprotein, RNA-dependent RNA polymerase (RdRp), the main Protease, the membrane protein, and the ssRNA virion as a unit.

Plugging the QGATD output into Drugbank.com's database, I came up with upwards of 50 drug matches. 

As research and clinical trial reports started trickling in, I found that most of the drugs that showed appreciable efficacy were among the candidate drugs I identified via QGATD. The list includes Dexamethasone, Remdesivir,  Quercetin, Vitamin D, Ivermectin, Artemisinin, and two of its derivatives - Artemether and Artesunate, to mention a few. 

This is IBM's Summit, the world's fastest supercomputer. In March 2020, researchers from the Oak Ridge National Laboratory, USA, tasked Summit with identifying existing drugs that could bind to the spike protein of SARS-CoV-2, which could limit its ability to penetrate host cells and spread rapidly. Using very complex algorithms and big data resources, Summit ran 3-dimensional simulations of over 8,000 compounds and identified 77 of them as candidates.

Summit has a computing speed of 200 quadrillion calculations per second, making it one million times more powerful than the fastest laptop. According to the researchers, it took Summit about two straight days to finish the task. 

Now, imagine what it would cost, in time and processing resources, if an average computer were to run similar simulations. The time cost alone will run into months or years. And that will definitely not augur well in a health crisis situation that requires quick response time. The processing cost, as well, will be far out of reach for most researchers in most countries.

Is there a unique and innovative solution that runs on a regular computer with the capability of achieving results comparable to Summit's in less than two days? The answer is an emphatic YES. That solution is QGATD.

The uniqueness of the QGATD innovation is also highlighted by the accuracy of its result as a predictive model for identifying candidate drugs.  

The QGATD solution is a unique innovation. As such, its target audience includes the entire value chain of the global therapeutics development community. To mention a few:

• Research scientists
• Health professionals
• Pharmacologists 
• Drug administration regulators
• Disease control agencies
• Public health policymakers
• The World Health Organisation

Therefore, by extension, the QGATD solution serves the general public.

Specifically, the QGATD solution helps the target audience to circumvent the drawbacks of the traditional computational approach to drug repurposing as discussed in the preceding sections of this entry. It helps them to accurately identify candidate therapeutic agents (drugs) in record time, hence guaranteeing a quick therapeutic response to new disease outbreaks.

I would also point out here that I use "Therapeutics" in a broad sense to include pharmaceutical and nutraceutical drugs, herbal medicine, and other complementary/alternative modalities like aromatherapy and sound therapy.

Firstly, we shall set up Big Data metrics and indicators to monitor the popularity of the QGATD solution within the scientific community in general, and the therapeutics design industry in particular.

Secondly, usage of the QGATD platform will be tracked to measure global adoption and penetration.

The business model supporting the QGATD solution will have pre-built markers for measuring success. We shall engage experts in these aspects.

Tw1. Ikwan Onwuka, Physicist, Technologist, Researcher

2. Alexsoft, Software engineering, Machine Learning/Artificial Intelligence Solutions, Big Data consultant

Ikwan Onwuka, the team leader, is a trained physicist and technologist. He holds a bachelor of technology degree in Industrial Physics and for many years, he has been an ardent researcher in physics-based healthcare.

Ikwan's interest narrowed to the field of therapeutics design during the 2019 Lassa fever outbreak in Nigeria. It was during this time that he conceptualized a new computational approach to drug development and repurposing based on Quantum Physics principles instead of the traditional structure-based models. His argument is that the structure-based models consider, to a high degree of exclusivity, the particle nature of matter. As such the underlying science is largely Newtonian with the consequence of producing less accurate results at quantum levels. 

From a physicist's perspective, most therapeutic targets and therapeutic agent molecules are quantum entities, displaying the characteristic wave-particle duality. Therefore, a more accurate drug design model must consider the more intrinsic wave-particle attributes of therapeutic targets and agents. 

When COVID-19 became a global issue in January 2020, Ikwan saw an opportunity to try his concepts on a global challenge. Being a technologist, Ikwan found a way to transform his scientific concepts into a scalable technological solution, and QGATD was birthed.

Ikwan applied a variant of the QGATD solution to design complementary /alternative therapeutics in the form of a sound therapy protocol for treating COVID-19. The sound therapy protocol was tried as a complementary modality in New Delhi, India with very promising results. News about it was published in India's foremost Hindi newspaper, Jagran. The New Indian Express also carried the news on the 4th of July 2020. Link below:

https://www.newindianexpress.com/nation/2020/jul/04/...

Alexsoft is a software engineering company with expertise in Machine Learning/Artificial Intelligence solutions. They are also involved in Big Data consulting. Alexsoft will be contracted and charged with converting the QGATD prototype designed by Ikwan into a full-fledged, web-enabled platform for in silico therapeutics design.   

Equal opportunity to all irrespective of all the usual classifications of bias.