Photo: Organon Latin America's medical director, Juan Marques. Credit: Cortesía del entrevistado.
By Isabel Reviejo
Despite coronavirus (COVID-19)'s devastating impact, the pandemic has had some positive effects, such as accelerating the long-awaited digitization of the healthcare sector. Now that society has seen for itself the advantages of moving elements of this sensitive sector to a virtual environment, the revolution is likely to continue. That is according to Juan Marques, Medical Director of Organon Latin America (a company specializing in women's health and biosimilars which emerged this June as a spin-off of the pharmaceutical company MSD).
In his opinion, innovations such as big data open the door to increasingly effective medicine and to a pharmacovigilance system that will allow faster response times in adverse situations. However, he warns that this can only be achieved by ensuring data confidentiality and quality, in a field where reducing the margin of error to the minimum is vital.
Every year we publish a report in the MIT Technology Review on the 10 technologies with the greatest potential to change the world. Among those on the list for 2021 is messenger RNA, the foundation of some of the most significant COVID-19 vaccines. What potential do you see this technology having in the fight against other diseases?
Messenger RNA technology has been a landmark achievement for medical therapeutics: it has enabled the development of a vaccine in record time, allowing for much better quality, mass production, greater speed at lower costs, and opens up the possibility of developing vaccines for a wide range of diseases. It is going to be a turning point because vaccines are no longer targeted against a virus, bacteria or parasite, but against a specific protein of them, which allows us to be far more specific and, therefore, far more effective.
In what ways are artificial intelligence and big data changing the biopharmaceutical sector?
Big data will eventually affect every aspect of the biopharmaceutical sector: drug development, analysis, medical prescription and even the dispensing of drugs. A crucial factor is ensuring the quality of the data, in order for it to be truly useful. Another important issue is the source. For example, if we are talking about fashionable gadgets [wearables], they will create a lot of big data, but you have to remember that our tolerance threshold for poor quality data is much lower than in other industries. A machine-translated text might have an 80% accuracy rate and we will still be able to understand it, because of its context, but when it comes to medicine, that 20% failure rate could be critical.
We have nice, inconsequential things, like a clock that tells you it's time to go. But a watch which tells you that you have a cardiac arrhythmia or that you have to go to the hospital is a different matter. The development of gadgets for heart rate, saturation… will continue to improve, and as we gain more reliable data, we will be able to interpret it better.
As these devices become increasingly popular, how should such large amounts of data be handled so as to ensure user privacy?
The trends are moving in the right direction, but don't expect more from a device than it can deliver. Will it be able to tell me what time I need to take medication? Sure, that's fine. But from that point to things that are far more critical, such as determining whether or not a patient needs to go to the emergency room, or whether they might be having a heart attack or not… When the patient's life comes into the equation, we need highly, highly reliable systems.
With regard to privacy, medical data remains confidential and must be treated accordingly. Care must be taken to ensure that the system can maintain that privacy. It is not just a matter of the patient accepting certain privacy regulations; it is also the country's responsibility or that of the healthcare system that is handling it. Besides, big healthcare data systems could also become hackable.
It opens up new horizons in the field of hyperpersonalized medicine. Which steps are you taking in this direction?
I would argue that hyper-personalized medicine is a doctor's dream. The idea is to be able to perfectly identify characteristics of each disease and each patient, what makes them different, and adjust accordingly. Let's consider the case of slightly rarer diseases. It is very interesting to be able to identify a protein, for example, that you didn't know existed in that patient and that a different treatment used for a different pathology could be effective for this one.
Furthermore, it would be great to have access to each person's metabolism characteristics to not only decide on medication, but also to decide if the appropriate dosage level is above or below average, resulting in more effective medicine.
To what extent would it be feasible to employ data for tracking the effects drugs have on patients?
The pharmacovigilance side is probably another of the most interesting changes in big data. Sometimes you hear complaints as to why an adverse side effect of a drug wasn't discovered before it became available, and it's a statistical issue. A study demonstrating the effectiveness of a drug may involve 10,000 patients. But if 10 million use it and there's a one-in-a-million adverse event, there's going to be 10 cases that I probably wouldn't have initially identified. Another important issue: using big data and artificial intelligence, I will be able to analyze potential reactions and cause-effect relationships very quickly, and speed up response times.
It still doesn't seem financially feasible yet. So, are we going to get there? My opinion is that yes, most probably, because there will be some applications that will make it very easy to report what has happened, and all of that will go to a centralized system, and from there you will be able to see if there is a trend, in real time.
Beyond COVID-19, what are the major challenges for the sector over the next few years?
Firstly, diseases with a high mortality rate. Survival has greatly improved in cancer patients, but there is still much room for improvement. The second focus, probably the most complicated, is that of neurological diseases; essentially, Alzheimer's. The third is neglected diseases, which affect developing countries, many related to viruses and parasites, and where all this technology we have been able to develop for COVID-19 could help. Finally, low prevalence diseases, which may not have a significant number of patients, but which have a high impact.
What challenges are you specifically facing in Latin America?
From a health point of view, one of them is unwanted pregnancy and teenage pregnancy, which is unfortunately a severe problem. Globally, there are 16 million pregnancies each year in girls and adolescents, and related complications are the second leading cause of mortality for this age group. Technology could help us in specific cases. We are working to see how we can create support systems for adolescents using applications, chats, forms of communication with which they can receive information to prevent pregnancy. Teenagers usually look on the Internet, but the most appropriate information is not always available there.
The digital landscape is encouraging companies from outside the industry to enter the sector, but it's not always that easy. In January, the venture created by Amazon, Berkshire Hathaway and JPMorgan (Haven Healthcare) announced that it would cease its activity. What do you think of these incursions into the sector and what lessons do you think this latest announcement has taught you?
The trend will continue and we are going to have more and more relationships with agents that we are not used to interacting with. As for those who do enter and don't succeed, they might well be very interesting ideas that just weren't at the right time, and they could work once the rest of the structure changes.
COVID-19 proved to us that we are moving at a different speed. Telemedicine is a very interesting example. It was driven by the crisis, and has been tremendously effective, so I think we are going to continue in this direction. But now we need to fine-tune these systems, and I think that this is where we are going to see some very strong players come in. Telemedicine is here to stay.
Is it possible for the big technology companies to come in and provide an integral healthcare solution? They do have the capacity and budget, but they must understand the complexity of healthcare and that patients are not numbers, they are people, so there is an emotional side that should not be neglected because of technology. Any telemedicine solution has to include maintaining human contact and warmth so that the patient feels well cared for. It will continue to be necessary to examine the patient using our hands, to converse and to observe the patient's body language.
The pandemic has demonstrated the importance of interagency collaboration in tackling a problem of this magnitude. What role will this play in future challenges?
It's not that collaboration didn't exist before, but that now it has become a lot more visible. People had this perception that companies were like enemies who couldn't talk to each other, and they weren't. The most important thing this crisis has shown us is that when we work together, we can accelerate processes. At the start of the pandemic, the horizon looked very dark because of the amount of time it usually takes to develop a vaccine. The crisis alleviated some fears and will facilitate this kind of interaction from now on, but it is important to understand that collaboration has competitive regulations that must be upheld.
What should a manager's role be in transforming the industry?
A manager has to understand gender diversity, generational diversity and diversity of thought. To do so, you have to generate a sense that ideas are accepted and a conversational environment that previously didn't exist. On the other hand, managers must have the ability to understand which technologies are being developed and consider how they may impact our business in the short, medium or long term.
Another characteristic would be a tolerance for mistakes, which managers didn't have in the past either. You might fail, but if the idea is good then you have to just improve it and see what happens.
Published by OPINNO © 2022 MIT TECHNOLOGY REVIEW spanish edition