The Future of Medicine Innovations That Will Change Healthcare | Sanj Talks

Introduction [Music] hello my name is maninder Hora and I'm going to speak to you about future of medicine um I am a PhD in bioengineering from Indian Institute of Technology Delhi and um I started um my career in biotechnology in the 80s in the 1980s and have been in this field for about 40 years and I've interacted with the best Minds in this field academics um industry folks as well as regulatory folks and you know what I've learned over this all the throughout this career is uh that you know the opportunity in this field is enormous and I have been because I started on the ground floor uh I have seen U when people were double guessing this technology they were thinking that this technology of uh producing medicines uh from bacteria from bacterial sources recombinantly may not be safe may not be even safe to work with and maybe not safe when injected into patients and may not work as uh the hypothesis went and um all of those things have not come true as they are safe to work with and they have been shown uh to be safe The Rise of AI in Medicine in humans and effective so biotechnology is the science uh of making medicines using bacteria or Mamon cells to to U grow into and you you clone the DNA uh and and produce those medicines um in the bacteria and then you purify it and um when um I was uh going into the field of biomedicine people warned me this is not a pure field this is not biology this is not engineering why are you wasting your time and I you know defied them some of them were my teachers uh who thought you know I should go into a pure science field in any case I ended up where I am and uh I'm going to um discuss uh the field of medicine and its emerging Trends and I have divided this topic into into two Focus areas the first Focus area is the medicines themselves and and then I'll come to digital health so uh talking about medicines you know from the Medieval Times the medicines U Were uh actually called potions back then because they were a concoction of U of extracts from plants and and you know spices and and such things and you know they were there is no reproducibility about them and they were it was a black box but the real medicines started coming from uh the early 1900s you know when uh the extracts from plants from animals like porine insulin the pig insulin is actually very homologous or very similar uh to human insulin so por porine insulin was the first insulin that was given to patients and even human extracts like human plasma during the World War II so those then were followed by a very significant discovery of penisin the penicillins were first derived from bacterial sources and uh they eradicated uh so um you know they eradicated the bacterial infection so effectively and saved so many lives and have been saving since then those lives after after that the the vaccines Telemedicine: The New Norm um were were discovered and these vaccines were then um derived from killed bacteria or inactivated bacteria or or viruses and then they were purified and given uh to patients uh to subjects thus eradicating smallpox and some other diseases um then came the advances in chemical synthesis and these molecules were synthesized pure molecules were synthesized such as aspirin such as ibuprofen such as chemotherapeutic drugs these were all synthesized and purified now these are single entities so they are much purer easy to characterize and also cleanly manufactured um then there was a little bit of a pause I'd say about 30 years then in the 1980s uh the um the the area of biom medicines which is uh near and dear to my heart uh of recombinant biotechnology came into being and these uh molecules revolutionized uh the medicine the field of medicine because these uh medicines are produced from cells so no one had tried to do this at the time and these bacterial cells or Mamon cells produced these medicines in quantities that were very small at the time to tell you the enormity of where we were and where we have come today is that in 1982 there was not a single medicine from biotechnology that was uh on the market in 1985 was the first time that human insulin was introduced from recombinant technology today there are 540 medicines that are on the market and 55% of these medicines were actually they came to the market in the last N9 years so that tells you that the early phase of research was the foundational phase and then um now it has uh come to a place where now most of the medicines that are coming that are from biotechnology this was followed by vaccines that were that came from biotechnology because the same technique was used Personalized Medicine: Tailoring Treatments now it was then replaced by um the field of and or augmented I should say by the field of monoclonal antibodies monoclonal antibodies are are proteins that are similar to what what the body uh does when an invading organism enters the bloodstream these antibodies then bind to The receptors in your body and destroy the invading mechanism and uh so there are this field antibody field is an enormous field now and it will continue to grow in the future at also this was uh then uh added to by antibody drug conjugates so what we did in that is that we take a drug that is normally toxic to tumor cells but if it's given by itself it not just kills tumor cells but also kills normal cells so what we did was we conjugate them to antibodies that Target the um this conjugate to the tumor site and at that point then the then the drug is released and it acts in the vicinity of the tumor that's killing uh the tumor and uh you know the toxicity of the drug is not not felt by normal organs so this field is also another field that will continue to grow and um then we have seen the Advent of of cells as drugs so these cells uh what what the what are these cells these cells are cells that are actually uh taken out of your own the patient's own blood then they are um engineered in the in the test tube or so to speak and then they are uh expanded purified and then go back into the patient and uh this has become you know one of the hottest fields in medicine um the issue here is what we call as the needle to needle time so the first needle that takes out blood from Robotics in Surgery you from the patient and the last needle that reinus uh the the cells that we have expanded and um so this time could be from weeks to months so one of the new and emerging Fields is um is called offthe shelf self therapy and these cell therapies actually take blood from donors and they are engineered and and they produce cells that are already activated and purified and available at the time when the patient needs it where it he or she needs it so this field is a very you know expanding field and it's it's uh in the within the field of personalized uh medicine and no medicine discussion would be complete without talking about the DNA based vaccines and we all felt the brunt of the covid-19 pandemic when uh we had to develop the the industry had to develop um in collaboration with Academia as well as Regulatory Agencies had to deliver had to develop the vaccines U to eradicate or to control this um pandemic there were 600 million cases of of uh covid-19 uh diagnosed and 6.3 million people or 6.6 million people died of this pandemic so you know the effectiveness of this these vaccines was was so good and it was so predominant that in two years 13 billion doses of these vaccines were given and now you know covid-19 is a distant memory which which is you know which tells you the power of of biom medicine another field uh that now I'm going to focus on is digital health and digital health is a is a catchall for number of things um which includes the application of artificial intelligence machine learning on the Discovery and Wearable Technology and Remote Monitoring diagnosis of of of diseases and as well as tele medicine you know remote monitoring variable devices so let me uh first talk about artificial intelligence AI artificial intelligence has is useful for cases where there's lots of data and it's too difficult to assimilate all the data and come out with these conclusion especially on the spot and so what we are looking for in in here from uh AI is that if we access all the medical records all the diagnos diagnos diagnosis that U the patient has undergone like x-rays like scans Etc um then can we come up with can the doctor on the spot come up with uh you know an intervention uh that is likely to work in into the in the patient so so this is something that people are working on in the drug industry uh people are working on um look trying to differentiate between normal cells and normal proteins versus dysfunctional cells and dysfunctional proteins so you know there there are 30 trillion cells in a human body and they produce 100,000 proteins and these proteins come in all shapes and sizes and the shape and the size of the protein determines what its function is going to be and if there is a manufacturing issue by the body the body is a big manufacturing facility and you know like any other manufacturing plant they make some bad copies and those bad copies may have a small difference from those are those of those good copies and those dysfunctional cells lead to a dysfunction U and a disease and so by Machine learning techniques we would be looking at you know it's institutions are already looking at the pattern of you know how Gene Editing and CRISPR many of these dysfunctions occur or what um prevalence and um based on that then we'll be designing interventions and this field is a hot field right now and it's likely to continue to be hot and continue to bring uh you know bring uh to patients um interventions that would be meaningful uh to improve their lives the next um the next topic uh that I that I think um that I want to cover is of clinical trial design so all all the medicines are first evaluated in the clinical clinical trial scenarios and those clinical trial the biggest issue with them is how to design a clinical trial how to choose the right patients which who can benefit from the disease U prevention or disease eradication that we are attempting and how do we get how do you get those patients so again Ai and machine learning tools will are being used to to seek out those centers which have a preponderance of patients with a certain disease which are available at a certain time of of the year and all those things uh are leading us to design trials where we can enroll those patients that are likely to benefit from our intervention in in you know in a quick time thus reducing the time and cost of drug development another issue is the diversity of patients when the the when the drug is already on the market then we are treating a diverse uh array of patients diverse in geography diverse in racial dis racial makeup uh age Etc and you know you what you do not want is a drug to work only in a subsection of the drug of the population so by um Ai and machine learning tools we um companies are already looking at at Future Healthcare Trends centers that have diverse uh patient uh pool that they can test the drug into and um uh thus reducing the time and also effectiveness of the drug let me touch on uh the let me touch on the issue of tele medicine now tele medicine is um is a field of medicine that is definitely growing you know fast fter than any other hospital um you know caregiving um field and tele medicine has already been used especially in the covid pandemic days when they had patients um which were you know which had a milder case of uh covid-19 uh but had other underlying diseases so they were monitored you know by uh virtual means and and this is likely to continue and um one other thing that I would like to mention before I you know close is that um is uh one last thing I wanted to say was that in this um in this talk I've touched upon a number of areas and I am um Conclusion confident and also hopeful that many of these uh many of these ideas Concepts medicines would would uh materialize during my lifetime and I hope that more will materialize uh in the lifetime of our next Generations thank you if you want to talk about anything at all then you know who to call just tell tell tell tell just tell [Music] [Music] [Applause] [Music] n [Music] [Applause]