With AI, Africa can be the new Switzerland; the global hub of pharma
Arguably, Switzerland is the global hub of pharma. Many factors come into play here but nowhere in the world do we have such a massive cluster of pharma activities from research and discovery to development and delivery. And the impact and influence are huge and global.
Africa becoming the global pharma hub may not be easy, but it is essential, if not existentially necessary. Consider this for a moment, Africa has about the highest global disease burden (25%) and yet has the lowest access (1.3%) to adequate healthcare resources according to recent WHO report. This stat is worrisome, should be unacceptable and requires urgent fixing! At the same time, the African population is increasing in many areas, and this means more people would likely need various health and pharma products. Importantly, there is this huge workforce available to go into pharma development from discovery to delivery. If only a handful of countries or organizations in Africa could get serious and put in the required effort, this could turn out to be the most transformative time for healthcare globally but especially for Africa.
To be clear, it may not be super helpful trying so hard to compare Africa with Switzerland just for the sake of mere comparison. No, that would mostly be useless. By the way, Switzerland is a country while Africa is continent way bigger than the whole of Europe. Moreover, Switzerland might be about the size of some of the smallest countries in Africa.
So what we should be trying to do here is some sort of healthy benchmarking to learn a few things which we think would be helpful in maximizing some of the opportunities Africa presents. This effort would be useful not just for Africa but for everywhere else, towards a healthier better world. There are quite lots of things we could learn and improve upon from Switzerland. However, it starts with the decision and willingness to work towards transformation. Good enough, Africa right now more than ever before, could have access to most of the resources necessary for transformation in healthcare, especially pharma.
Why Artificial Intelligence (AI)?
AI, as we may be aware, is basically the concept of machines doing things humans normally associate with intelligence. A computer can perform operations analogous to learning and decision making in humans. We could describe it as the science and engineering of making intelligent machines, usually smart computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. We could see intelligence as the computational part of the ability to achieve goals in the world. So machines have been developed or programmed to carry out creative processes, and these are broadly grouped as AI.
There are quite a lot to AI, but currently, the major hype around AI is due to a technique called deep learning (DL). DL has found use in many industries and facets of life from communication to transportation, from customer service to finance and from manufacturing to healthcare and beyond. In summary, true AI is when a system can understand and learn from elaborate sets of data while making valid recommendations and suggestions.
Worthy of note here is the word “data” which is information or, to be specific, digitally available information. AI relies a lot on the availability of data to train the machine much like humans learn by taking in and analyzing data. The difference here, at a fundamental level, is the idea of machines learning with vast amounts of data over a much shorter period at an accelerated speed. So it is about trying to accelerate and optimize the rate at which machines take in data, analyze the data and learn from the data in order to make more accurate and valid recommendations and suggestions where needed. Overall AI could have potentially disruptive applications, and implications for the current architectural order of the global economy and places like Africa should prepare to take advantage of this as well as make necessary inputs.
In healthcare, AI applications are increasingly being used in almost every area including diagnosis, surgery, patient monitoring and of course drug development and delivery. At the fundamental level, this could be described as harnessing the power of big data in the life sciences which involves processing and displaying huge volumes and varieties of health or biological data at a rapid rate. Currently, this is an increasing trend in many healthcare sectors where there is a vast amount of data such as lab data, insurance data, patient records, and research data. In a recent report, CB Insights identified over 100 reputable companies that are applying machine learning algorithms and predictive analytics in various facets of healthcare to achieve things like reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.
Currently, many major medical and pharmaceutical companies are already harnessing the power of artificial intelligence with great results. For example, Johnson and Johnson’s Sedasys system has received FDA approval to deliver anesthesia for standard procedures like colonoscopies automatically. A doctor oversees multiple machines at once, making the cost much less than a dedicated human anesthesiologist. Insilico Medicine has taught its AI system to predict the therapeutic use of new drugs before they even enter the testing process. There are also numerous robots in various stages of testing and approval for diagnosing disease.
In some cases, such as with IBM’s Watson, these machines have a higher accuracy rate for diagnoses than human doctors. According to Harpreet Singh Buttar, an analyst at Frost & Sullivan, “by 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries.” At the core, AI facilitates accessibility, relevance, and actionability of healthcare information.
AI in Pharma
“Medical decision making with artificial intelligence using the power of supercomputers will change everyday medicine. Cognitive computers, such as IBM Watson, have been used in many ways to analyze big data, not only in genomic research but also in biotechnology. This will also shape the way new drugs are found. It might lead to the end of human experimentation through detailed simulation of human physiology. Our era, with drugs being tested on actual people, will seem barbaric to people of the future. What if supercomputers could test thousands of drug targets on billions of simulations modeling the physiology of the human body in seconds? Pharma should support such research for their benefit.”- Dr. Bertalan Mesko on the Future of Pharma
At this point, it may be important to note that AI and many other tech advances are fundamentally just tools, tools to solve problems and make things better. So the question was: Are there problems in pharma that need to be addressed? Are there pharma processes that need to be improved, enhanced and made better? Well, anybody close to pharma knows there are long-standing problems seeking solutions and processes deserving improvements right from drug discovery and developments to drug delivery.
Let’s look at drug discovery. In nature and within pharmaceutical companies are vast amounts of molecules and compounds that could be suitable solutions to combat specific diseases and improve health, but the challenge lies in identifying them as such; as potential therapeutic entities. Drug discovery and development is likely not only the most prominent challenge but also the most significant opportunity for improvement in healthcare. Finding a new drug could be very demanding and cost prohibitive. It takes much time with substantial financial and intellectual demands. At the basic level, these are usually due to the necessary processes needed to ensure we have drugs that are effective and safe for use. On average, the processes involved in making drugs available, from discovery to administration, could cost pharmaceutical companies up to $2.6 billion and could take about 12 to 14 years to complete. Due to this, the leading short to long-term applications of AI in pharma is more towards reducing the time and hence the cost of drug development. This would not only enhance the return on investment and reduce the costs for users but would, in fact, be helpful in making useful products available faster, especially where it matters most.
As a matter of fact, thousands of molecules are usually studied and passed through lots of processes and from which only a handful could get to clinical trials, with maybe two to none getting approved as drug out of about ten thousand molecules studied on average. The questions then are: Is there a way AI could quickly help pharmaceutical developers avoid spending too many resources on doomed to fail molecules? Could pharmaceutical developers focus more on just a handful of the most potent molecules that could end up being suitable and approved for their specific purposes? The result would be drastically cutting down on the amount of resources spent, accelerating the drug discovery process and ensuring that better quality drugs are discovered. Well, it turns out AI could be helpful and AI, as we acknowledge, is currently finding applications in almost every aspect of the drug discovery process.
A handful of AI focused companies including, Insilico Medicine, Atomwise, Numerate and others are doing much work and processing large clinical and medical data to help the pharma do better. Many, including Frost & Sullivan, have recently recognized the effort at Insilico Medicine. Even with the current rate of progress (and the rate of progress is actually accelerating), there would likely be a reduction in the cost of medical treatments by half across the board within the next couple of years. This would likely transform pharma and healthcare overall.
New Dawn for Africa; transforming into the global pharma hub
There has been this recurring theme among some people in Africa, in both high and low places. They are of the opinion that it would be near impossible to do serious research, develop drugs and make more original contributions to pharma at a global level from Africa. Many of these people usually cite the prohibitive and increasing costs of drug research and development as something many African organizations interested in pharma cannot afford. For them, African institutions are bound to rudimentary research efforts that are mostly bound for the shelves. Many acknowledge this as utterly unsustainable. Thankfully with the aid of advances in tech especially AI there is now absolutely no reason why scientists and developers in Africa cannot be more productive and innovative towards achieving better drug discovery outcomes.
We see that the digital revolution in medicine in Africa, like most other parts of the world, produced a paradigm shift in the healthcare industry with the increased availability of useful medical records/data for improved efficiency and quality of care. Although as a matter of fact, Africa is yet to adopt digital healthcare in many areas adequately. However, as noted earlier, some useful data including biomedical imaging, laboratory testing such as basic blood tests, and other biological and omics data as well social data could readily be applied to new technologies like AI and blockchain to enhance and scale up the progress in healthcare sciences for more effective and cost-efficient healthcare ecosystems. Moreover, considering the inherent challenges in Africa, we could say this intervention is urgently needed in Africa.
Some of the specific things we could do to transform pharma in Africa with AI include but not limited to; generating highly potent molecules through thousands of chemical combinations, development of relevant biomarkers and drug targets, tests molecules on thousands of drug targets on patient models, monitoring drug administration and analyzing delivery outcomes. However, to apply AI techniques like machine learning algorithms at scale to reduce drug discovery times and costs requires building a critical mass of talent and knowledge base in relevant areas. This is fundamental to putting Africa on the path of relevance in drug development globally and entails having scientists and developers who have a firm understanding of relevant sciences and able to apply useful tech to ramp up processes.
As we move forward with technologies like AI, this apt insight comes handy; “innovation in the healthcare space should move forward using the IT capabilities already in play through human-centered design thinking,” writes Lyle Berkowitz, M.D. at his “Change Doctor” blog. Here are some recommendations the pharmaceutical industry can use to facilitate the adoption and implementation of innovations like AI:
· Start small: “Little bets make for big wins,” says Berkowitz. For instance, it is reported that when piloting an iPad initiative at his practice’s inpatient oncology floor, Berkowitz said they started with only five devices. Doing the pilot on a smaller scale helped them learn about workflow and network issues. The recommendation here is, rather than not start at all or wait until every light is green, Africans in pharma should begin wetting their feet immediately by looking at basic applications of AI in pharma and then delve deeper as they gain more understanding. So it is important to at least start and the time is now.
· Tech is not a silver bullet: Behind technology are the process and people, and that is what advances innovation. Usually, some of the most useful innovations involve designing workflows that would use one tool to link everyone in the organization. The recommendation is always to see tech, in whatever form they are, as tools, tools to solve problems and make things better. AI is a tool. However, as tools, they do not always replace all the people and processes. Nevertheless, it is essential for the people to be equipped to use the tools well.
· Look outside of healthcare: Industries outside healthcare are way ahead when it comes to technology, and healthcare can learn from them, many would agree. We should look at other industries like transportation or communication and maybe some of what they do would ignite rapid innovation which could be applied to pharma. It might help to spend some time in a different industry, look at what they do to increase satisfaction and efficiency and try to optimize some of these ideas and bring them back to healthcare. We need to realize that a critical attribute to doing better with tech is keeping an open mind along with learning to think outside the box. We should be willing to learn and collaborate with strengths as much as possible. We should be willing to borrow progress wherever possible.
· Learn from others in the industry: Some of the most forward looking people in pharma are already taking notice and moving to maximize the benefits of AI and Africans need not be reluctant to do the same. It is important, especially during the early stages of adoption, to communicate and learn together to avoid reinventing the wheels or making mistakes others have made, when ordinarily the mistakes could be avoided. We should discuss developments such as the use of electronic medical records, analyses of medical insights, applications of medical research and innovation in pharma and overall continuous improvement practices in healthcare. The recommendation is not to copy exactly what others are doing but to see if some similar approach could be helpful. At times, adopting tech could be challenging especially in organizations and industries with a history of low tech adoption. However, some tech like AI transforms everything and organizations not willing to make progress risk being left behind. Just as earlier noted, we need to collaborate, learn and share knowledge as much as possible both from outside and within the industry.
In pharma, Switzerland has many lessons for everybody everywhere especially in Africa, and we should seek to improve on some of these especially on the drive towards pharmaceutical excellence. At the core of this success is excellence in R&D and so far we have shown how pharma in African countries could leverage AI and other tech developments to accelerate meaningful pharma R&D. Apparently, Switzerland is also able to attract and capitalize on lots of other pharma progress and developments in other parts of Europe and the rest of the world. This advantage would require being open and seeking out innovation globally. This openness to creativity and innovation may not just be helpful but critical to indeed becoming a global hub.
The value of Human Data
We cannot overestimate the importance of data for AI developments. Human data like basic blood tests and biological information like genomics as well as conventional data like social media data are beneficial for various AI applications in research and development. Thus, a key consideration is the availability of data. In fact, as we may now be aware, AI developments took off in recent years due to the availability of massive amounts of accumulated data made possible by the use of computers and other information and communication technologies.
In Africa, much like in other parts of the world, vast amounts of data are being generated on a daily bases. It is not that the data have not always been generated; we have ever had data. However, advancements like digitization of medical records made it possible to have some data in a form that could be easily married to tech developments like AI for even more accelerated tech developments and transformation of society. There is this new saying; “data is the new oil, and AI is the new combustion engine.” Also, some others say; “data is the new gold.” So the question is: How do we ensure we continue to make useful healthcare data available in Africa? There is more to making data available but the most immediate thing we could do is encourage digitization of the various healthcare practice and research processes. This is necessary as we work to transform pharma in Africa.
Giving Back the Power over Data to Individuals in Africa
While data could be accumulated easily and at times for free, it could also be hard to collect quality and relevant data for some AI applications when needed. To solve this, we have proposed a “Personal Data Market” as a means of making it easier for developers to acquire data whenever needed. This would not only benefit the developers who need data but also directly benefit the patients or individuals. This is because people would get some payments as an immediate incentive to help provide data and would likely provide more data as relevant. It is sort of “Data Monetization” in the interest of all players. For instance, the people could be encouraged to go carry out blood tests to generate data which would then be helpful in health research and for making healthcare solutions more personalized and better overall. A patient could go to the clinic and do blood tests and then upload her blood test data on platforms like Longenesis.com or maybe Young.ai or Aging.ai to get insights about their health. With Longenesis.com, people get some reward which enables her to buy a few things for the family. With this, she and more people could be encouraged to do even more tests to help improve the applications. Users also end with improved health; good for individuals and good for society as a whole. Deploying these applications then assists in the provision of better healthcare overall.
The most significant achievement here, beyond advancing healthcare, is that the power over data is given back to owners of the data, the patients, and individuals. They get to control who gets to use their data and for what, in addition to being rewarded whenever the data is used. This is contrary to the current situation where various organizations mine digital data without the consent or permission of the data owners and without rewarding them for the generated data.
The new platform Longenesis which is being developed by a group of partners works to improve some of these. It’s important to note that this has the potentials to transform healthcare in Africa massively. This would possibly usher in a new healthcare paradigm for Africa. Fundamentally, people are rewarded for taking care of their health and helping to make healthcare better, for instance, when they provide relevant data for AI and blockchain applications development and more.
This would help professionals, patients and everyone else take care of themselves and those around them. With this immediate and direct incentive to help make healthcare better, we would likely see significantly better health outcomes in the nearest possible future. Of course, there could be challenges in the adoption and implementation of this platform. It might seem strange or maybe unheard of to most people. Some might even be reluctant to take part, especially in the early stages. However, optimistically, people would quickly realize how these developments could be helping everyone live better lives, and then most people would want to take part and derive the benefits.
Looking Up and Forward; the Future is Now!
The future of healthcare is preserving health not merely curing diseases. Many people believe that healthcare is broken and some agree that the most significant answer is putting the power back into the hands of the patient. This is part of what Longenesis is trying to achieve by converging AI with blockchain to accelerate and decentralize healthcare developments and practice. Increasingly, there are devices under development that could use AI and big data to let everyone self-diagnose. Sequencing of individual genomes and then comparing them to a vast database will allow doctors and AI bots to predict the probability that you will contract a particular disease and the best way to prevent or treat the conditions. An example is the Tricoder device presently under development by some individuals and organizations. Companies including Google, Apple, Samsung and many others are investing a lot in developing new biometric sensors.
Meanwhile, back in 1985, the PUMA 560 robotic surgical arm successfully assisted in a delicate neurosurgical biopsy. And then about two years later, the first laparoscopic procedure — a cholecystectomy — was performed using the robotic system, and in 1988, PUMA was used to perform transurethral resection. These landmark surgeries opened up the potential for a higher degree of precision in minimally invasive surgeries through the steady, mechanical hand of the robot. At the same time, there are AI devices and apps that could do vital and straightforward things like reminding you to eat well, take your medications, or get necessary tests that could help people live better. The information from these sensors and vast amounts of data could help prevent diseases and enhance longevity which is what Africa needs.
So we have all these exciting novel areas that are having increasingly better prospects. Historically, when new technologies become more accessible to use, they transform industries. This ease of use is precisely what is happening with tech developments like robotics, genetics, VR, Internet of Things (IoT), big data and most importantly AI. As barriers to implementation such as cost, tech know-how, computing power and accessibility continue to go down, these new technologies would continue to find application in many more areas especially healthcare. More organizations will find better ways of adopting and applying them. It takes an open and innovative mind to explore them and make good use of them early enough, and we in Africa cannot afford to be left behind this time around.
With AI in pharma, we are primarily employing the data-driven approach where we could say math meets biology. This is poised to accelerate the process of discovering drugs especially for the hard and complex diseases including tropical diseases in Africa. Interestingly, analyses of vast amounts of biological data allow better insights and more efficient drug development. However, beyond drug discovery, AI is also useful in other aspects of pharma such business and marketing based decisions such as analyzing and assisting with mergers and acquisitions and providing guidance on the most efficient and effective way to market new products.
We have noted some of these throughout this discussion but using AI to accelerate pharma development in Africa would undoubtedly bring many benefits including;
It would reduce costs.
It would create more jobs.
It would reduce fake drugs.
It would encourage more use of the indigenous and available talent, development of more talent and transfer of expertise.
It would encourage more use of the indigenous and available science and tech, development of more science and tech and transfer of more science and tech.
It would demonstrate in some circles, once and for all, that Africans are equally good and can make good stuff happen.
It would improve the quality of products available and improve the standard of care.
If we are serious about making quality products in Africa, people in Africa and beyond will naturally use more African products and hence the incentive to make even more and better products in Africa.
Overall, we would have a better Africa and a better world.
By the way, we try to go speak on some of these in a handful of countries in Africa. We attend quite a good number of events and it’s been impressive overall.
Although it might seem like AI is in its infancy, this convergence of math, technology, biology, and healthcare is rapidly transforming the pharma with progress continually being made in developing systems with real-world value. While there are conflicting arguments and opinions on the challenges and uses of AI, the specific application in pharmaceutical developments; to increase efficiency and effectiveness could only be laudable. Should we be using AI here just because we like tech? No! The fact is, AI is a tech that is profoundly transformative and is helping solve problems and make things better. Which means it would be a matter of doing ourselves and those who need helpful and affordable pharmaceutical products a considerable disservice if we do not seek to apply tools that could be helpful.
Currently, many African countries do have much going for them. There are investments in healthcare, and many are working towards Universal Basic Healthcare. There are increasingly educated populations who have historically demonstrated strengths in pharma and other aspects of healthcare, although usually outside of Africa. So the questions are: Can we scale this progress within Africa using tech developments? Are we willing to deploy efforts and resources necessary to maximize developments in technology?
Well, these and more, we believe, when adequately implemented and scaled in a handful of African countries would see a massive cluster of pharma efforts and progress that would vastly transform healthcare in Africa and beyond. So as we say; the time is now.
About the Author
Iraneus Ogu directs the Africa Artificial Intelligence and Blockchain for Healthcare Initiative at Insilico Medicine, Inc. In addition to tech developments, he works on Longevity and Aging Interventions with his research efforts focusing on neuroregeneration. He also has a background in Pharmaceutical Sciences at the University of Greenwich where his research focused on controlled-release dosage forms.
