FORWARD

Welcome to the Autumn 2021 edition of Pharmafile. 2021. What began a year of great hope for us all has seen plenty of challenges, not least the pharmaceutical and healthcare industries.

They’ve faced a rapidly changing and evolving virus, a rise in antimicrobial resistance across infectious diseases, and the necessity of engaging increasingly via remote channels, with all the associated limitations. All the while, rare, chronic, and infectious diseases have needed addressing through innovative treatments and therapies. Kate Pain, Associate Director of Digital Strategy and Capabilities at Astellas, and Sebastien Noel, Director of Multichannel Strategy at Veeva, gave us insight into healthcare professional (HCP) engagement at a time when our relationship with health is constantly changing, and the needs of patients are both precarious and increasing.

Nine out of ten HCPs are claiming they would prefer either all virtual, or a mix of virtual and in-person meetings. Sebastien Noel, Veeva, shares: “The long-term implications for the industry with the dramatic shift to virtual, or hybrid, engagement are huge. I believe that the quality of relationships between reps and HCPs is the one big positive that has come out of this enforced shift to virtual. The ability to have more one-on-one facetime with HCPs is now enabling reps to present more content in a more streamlined way, in a way that works and is convenient for both parties.”

Kate Pain, Astellas, shared that the shift “requires an exponential revision in the way that pharma companies deliver their services. We need to ensure that our field colleagues have the tools and capabilities to be equally confident in hosting virtual or in-person meetings.”

It is not always easy to meet these demands, however: “We need to be proactive, not just reactive, so that we are primed for change and can pivot in accordance with environmental shifts and/ or customer preferences. One size won’t fit all, which will add further complexity, but it also adds colour and opportunity for pharma companies to differentiate themselves from the maelstrom, and shine.”

“In 2021, almost 70% of HCPs are digital natives,” Sebastien elaborated, “so providing a personalised, hybrid engagement model is needed to support meaningful interactions. With 87% of HCPs now wanting either a fully virtual or a hybrid meeting experience post-COVID-19, it is important that life sciences companies personalise their engagement channels to meet different HCP needs.”

But how might a company do this? Kate gave us some insight into how Astellas addressed the challenge of a rapid change in HCP needs: “When the pandemic hit, we jumped from first to fifth gear in terms of our digital deployment. We prioritised our focus on three core channels – Veeva Engage, our HCP portal Astellas-Pro, and rep-triggered e-mail. Stripping these three must-have channels out from the niceto-haves enabled us to narrow our focus and maximise our impact. We were able to preserve a line of communication with our customers, and ultimately with our patients.” So, organising communication is key: “Between March and April 2020, Astellas deployed Veeva Engage to 44 countries in just six weeks. Segmentation was key to the speed of the roll-out. We separated the 44 countries into 4 waves, split by their size and digital maturity, so that we were rarely managing more than 10-11 affiliates at one time.

“For our first release, we set our sights on mastering the basics. We enabled only the basic functionality within Veeva Engage. Now, on our fifth release, our offering has evolved into something significantly more sophisticated. As a business, we were able to differentiate between the immediate need versus a longer-term aspiration.

We also adopted standardised modular templates, a single source playbook, and maintained strong communication among project teams and affiliates throughout.

This activity, paired with our deployment of the other musthave channels, kickstarted a progressive digital mindset that was to continue for the next 18 months, and has left an indelible imprint on our commercial model moving forward, with a commitment to delivering omnichannel excellence.”

There are significant benefits to remote engagement, Kate argues: “One of the greatest advantages of remote engagement is that meetings are typically longer than in-person calls. The average face-to-face call is 6 minutes and the Astellas average for a virtual call is 26 minutes. It’s essential to provide soft skills training to support these longer interactions and to increase confidence levels with the technology also.” These benefits exist alongside hurdles, however: “we experienced access issues in select healthcare organisations, which deterred customers from connecting with us via Veeva Engage. This was experienced in Germany, Spain, and the Adriatics and Baltics. In this case, personalising our invites to include Zoom meeting links overcame the issue, and since then more than 8,000 HCPs have connected using these links.”

Further difficulties do seem to arise in relation to geography: “Challenges may also arise when operating across different countries, as the adoption of remote technology and response to COVID-19 has not been consistent around the world. Some reps and medical scientific liaisons have viewed it as a temporary fix until face-to-face interaction resumes, while others have been quicker to welcome it as a long-term solution.”

So, what is the future of HCP engagement, in such an uncertain landscape? Is the virtual solution temporary, or the new way of liaising with healthcare professionals? “It’s too early to predict what the pattern will be post-pandemic,” Kate told us. “From April 2021 onwards, we’ve seen in-person interactions increasing, however the numbers fluctuate monthly, and the face-to-face/virtual frequency has yet to find its balance.” With the emergence of the Omicron variant, and winter approaching, the status of face-toface meeting is again looking uncertain. “The critical point is that we need to deliver according to our customers’ preferences,” says Kate. “We need to get better at listening to them while also learning from early adopters outside our sector to provide effective solutions.”

Sebastien believes it likely that “over the next five years, face-to-face meetings will undergo a gradual resurgence across Europe, but virtual meetings will stay. This is because virtual meetings facilitate longer, more meaningful HCP interactions and deliver greater flexibility when scheduling several appointments in the same day. They also provide an alternative means to interact when face-to-face meetings are cancelled or disrupted last minute.

“With the life sciences industry now acutely aware of these benefits, I predict that an omnichannel approach to HCP engagement will become increasingly popular over the next five years.”

The pandemic has highlighted the growing need for DNA vaccines, in multiple areas including infectious diseases and cancer. Looking ahead to the future of healthcare during the precariousness of a pandemic, we spoke to Professor Lindy Durrant, CEO of Scancell, which develops immunotherapies simulating the body’s own immune system response. Scancell believe that DNA vaccines are one component of tackling diseases, believing the vaccines “give more sustained production of antigen which should lead to longer term protection than RNA vaccines.” This vaccination form is significant for the increasing demands and needs of global healthcare: “Dendritic cell-targeting DNA vaccines are cheap and easy to manufacture, and have long term stability at -20°C. They can be rapidly adapted to give protection against both infectious disease and cancer. They can be used for repeated injections or for injections for other viruses, whereas viral vector-based vaccines can only be used twice before the immunity to the vector overwhelms the response to the inserted virus.” For the many and varied needs presented by patients of infectious disease, this is significant.

Scancell uses a human monoclonal antibody to target cells that stimulate immune system response. This vaccine can be customised against a specific disease – a promising feature considering the changing nature of disease we have witnessed over the past year, and even the past month. This has the potential to change the current impact of vaccination. “A vaccine encoding both nucleocapsid protein linked to a modified Fc and receptor-binding domain should stimulate long-term memory B and T cells, and should give sustained protection over several years. This will give less opportunities for variants to arise and prevent people from having to be vaccinated every six months.

Scancell shared that its vaccine “targets antigens to dendritic cells to give more potent T cell responses that lead to long-term memory, and still give high titre neutralising antibody responses. The nucleocapsid protein is more highly conserved, giving better protection against variants.”

As for the future, Professor Durrant looks forward to “simpler delivery systems for DNA vaccines. Currently we use a mechanical needle free device which is acceptable, but a simple injection would be more reasonable”. Durrant also hopes for “better investment in vaccines that give broad protection (i.e. all coronaviruses – not just COVID-19), and sustained protection for 5-10 years should be developed now so they can be rapidly adapted when the next pandemic hits.”

With thanks to Veeva, Astellas, and Scancell for their contributions.

EXCLUSIVE INTERVIEW

Pharmafile speaks to DeepDrug

Dr Kishor Wasan:Artificial intelligence (AI) is like a child where you’re feeding it new information, and as it processes new information it develops an evolved algorithm based on that new information. As we get information about the variant strains, the AI algorithm can adjust to virus strains and spit out new compounds, or new molecular targets, to address it. But there’s a key point here: many people look at viral proteins to determine as potential targets, DeepDrug looks at human proteins. In other words, what are the proteins that the virus uses, so that it can enter, replicate, and transmit itself. And so many of the variant strains use the same human protein to do its business. That’s how we adjust to the new strains of the virus.

For greater than 50% of all drugs made to the market, the toxicity profile kills a product in drug development, because of nonspecific dose dependent toxicity. We can, early on, understand and identify which drugs are going to be too toxic for animal studies and ultimately human studies early on. AI not only predicts efficacy and drugs that might be very effective against the virus, but we can also predict what drugs at concentrations and doses that we want to see efficacy might actually be toxic to patients in vivo, and will lead to drug interactions. We have to confirm this work with wet lab pharmacology, we don’t jump straight from AI into patients. But what we do is eliminate early on what drugs are going to be toxic, what drugs we think will be toxic at the doses that we need to show efficacy.

Long haul syndrome is becoming a bigger and bigger issue, and we’re still gathering data about it: we’re only 18 months into the pandemic, but we’re seeing that around 30% of patients might have some kind of longhaul syndrome, lingering effects, long after they’ve recovered from the virus. A lot of it has to do with the effects on their cardiovascular health, their mental health, as well as other things. We think if our drug combination can decrease the length and severity of the symptoms – once you get COVID-19, instead of having to suffer for 10 to 15 days and then afterwards have these lingering effects – if we can cut the symptoms of severity down to a few days, as opposed to a few weeks, it will have a long-term impact on organ damage and mental health. When you have long, lingering effects of a disease weeks or months after you’ve resolved the disease, I think that’s what taxes your mental health, because you’re still suffering. The direct effect on organ damage, and the indirect effect of the lingering effects on your mental health, is why we think this could help with mental health issues in the long run.

DeepDrug was working in emerging infectious diseases for a number of years, and we pivoted to COVID-19 eight months ago. But the whole idea and the premise is if we understand how viruses work, we actually know how many of these viruses utilise human proteins to replicate entry, transmit, etc. With that fundamental knowledge already in our back pocket, we’re going to be able to be ahead of the curve when a new emerging disease comes out. When a new disease emerges, we look at how the disease works, compare it to what we have in our algorithm, input into the AI any new features about that virus or disease that we don’t know about. From that already built algorithm, which is evolving and growing, we can then make predictions to deal with those emerging infectious diseases and viruses.

I’ve been involved in systemic fungal infections for 30 years. Many of us in the infectious disease community have said that the cupboard has been bare: we have not had a new set of drugs or drug compounds to treat a variety of infectious diseases for a very long time. In the field of systemic fungal infections, we’ve actually not had any new drugs, with new mechanisms of action, to deal with some of the new emerging systemic fungal infections, ones that have drug resistance, in the last 20 years. Looking into the future, if the pandemic has done anything else, it has really woken everybody else to the fact we need to fill that cupboard up with new drug therapies with new mechanisms of action. Moving forward, there will be a reemergence of research and development of new infectious disease drugs, using AI and other routes, so that we don’t get caught with what has happened in the last 20 years in infectious disease.

Dr Supratik Mukhopadhyay:I first had the idea of using AI to accelerate drug discovery around 2007. However, at that time, AI was not really as mature as it is today – the AI revolution had not started. Also, I didn’t have access to high-performance supercomputers. So I let the idea germinate. In 2009, I joined LSU and had access to LSU supercomputers (that was one of the reasons why I joined LSU). By 2012, the AI revolution had started. I also found a fantastic collaborator in Professor Michal Brylinski who had just arrived at LSU. At this point I decided to act, starting the DeepDrug project.

In fact, it’s emerging out of what we’re trying to do with the neglected global disease world, and what we’re trying to do with more efficient drug development through AI.

I’m the co-founder and co-director of the neglected global diseases initiative at the University of British Columbia, which we formed 10 years ago. For us, the idea was to develop drugs at a low cost for developing world indications. The definition of neglected diseases is that they have a huge number of patients or people that are infected with the disease, but there’s very few resources to treat the disease. What I hope will happen is that with AI, we can develop new drug molecules more effectively, more efficiently, that are cost effective, that we can then go after many of these neglected global diseases that we really haven’t spent the time on. Unfortunately, many companies are trying to make a profit, and for neglected diseases, you’re working in countries that don’t have the economics that pay for those high dollars. I think that’s where AI comes in. Because we can develop cost effective drugs that can get to millions of people for these neglected diseases.

There have been several accomplishments along the way. We broke up four adenosine receptor antagonists into their molecular fragments and then recombined them to create an adenosine receptor molecule. Adenosine receptor paired with various G proteins have effect on inflammation, pain, and immune responses. By 2016, we entered the IBM Watson Artificial Intelligence XPRIZE and by 2020, we had reached the semi-final of the Artificial Intelligence XPRIZE (among 142 teams worldwide). In February 2020, I gave the semi-final presentation at the TED World Headquarters at New York City. After we came back, we saw the first COVID-19 wave hit the US. Soon, there was a lockdown in Baton Rouge. At this point, we decided to pivot our engine towards therapeutics discovery for COVID-19. Today, a combination therapy is undergoing human trials both at the Riverside University Health System Medical Center in California and in Europe.

I will give an analogy here. If you see the time period between 200 BC and 1500 AD, science and technology progressed at a very slow pace. For example, all through this period our mode of transportation was horsedrawn carriages/horses and sail-driven ships.

However, in the last 300 years, suddenly you see rapid progress in science and technology. We went from horse-drawn carriages/ horses and sail-driven ships to high speed locomotives, cars, aeroplanes, and rockets. What changed?

Let us look at physics. Before the 17th century, physics was mostly descriptive (it was called Natural Philosophy). All that changed when Sir Isaac Newton invented calculus that brought in the quantitative aspect in physics. Now you could compute and predict.

I believe that biology and pharmaceutical sciences, have been, for a long time, like physics before 17th century, largely descriptive. What AI brings is the quantitative aspect. You can now compute and predict. This can change the nature of drug discovery.

Today the time and cost needed to take a new drug to the market from scratch is 10 years and $2.5 billion. A lot of this time and cost required is due to the fact that only 12% of the drugs developed by pharmaceutical companies succeed. As a result, pharmaceutical companies were reluctant to develop new drugs for sectors that had low profit margin, e.g., infectious diseases. In fact, the last new antibiotic was developed by the pharmaceutical industry in 1987. AI can not only accelerate the identification of hits but also improve the success rate drastically. You will now be able to take new drugs to the market at a fraction of the time and cost.

I think AI will be tightly integrated into the drug discovery pipeline. There will be renewed focus on drug discovery for infectious diseases and other neglected global diseases. AI-enabled drug discovery pipelines should be able to handle pandemics in a much better way. In addition, given the ubiquity of genomics datasets as well as EHR data, I anticipate the rise of personalised therapeutics.

We will see pandemics, this pandemic and even the next ones, getting rapidly addressed. We’ll see new types of medicines and vaccines, for things like Dengue, cancer, and lung damage, for which we have hardly any medicine to treat. Treatment for these kinds of diseases, neglected diseases, will see a rapid advancement. Moreover, we will see a rapid advancement in our understanding of biology.

AI can help repurpose generic drugs and their combinations towards emerging diseases, providing affordable therapeutics for the world as opposed to expensive treatments like monoclonal antibodies. AI can help develop environmentally friendly drug manufacturing processes. In addition, AI can help discover lipid nanoparticles with certain properties that make them suitable for drug delivery. AI can find therapeutics based on generic drugs: there are thousands of generic drugs on the market today, and it can see if any of these generic drugs can help us treat diseases, or in combination can. And that immediately changes the potential cost of pharmaceuticals, because generic drugs are cheap.

Another thing is nutraceuticals: there are plant extracts, natural products, plant extracts and other minerals, that may be suitable for prophylactic, immune, purposes for different diseases. COVID-19 may be one of them, and maybe other diseases, like flu, and so on. Advantages here are an environmentally friendly manufacturing process, and price – these plants can be grown in mass in different parts of the world.

AI can revolutionise global health, and it can revolutionise disease diagnosis. Combining the two potentially gives patients access to personal therapeutics.

There are two modes of operation, one is creating new molecules completely from scratch, the other is repurposing existing FDA approved drugs. For new drug development, DeepDrug can save 30% of the time. However, through drug repurposing, DeepDrug can save 90% of the time. Because DeepDrug can already filter out candidates that are likely to fail, you will need less in vitro testing, and less in vivo testing. It helps rapidly take a drug to human trials. Because of the drastic reduction in time and cost, pharmaceutical companies can now attend to neglected global diseases that have so far received very little attention.

Dr Bruce Weng:This is intervention postinfection, so it doesn’t replace preventative therapy, which I think is the most crucial intervention. I think a lot of people may look at interventions that are now available and say ‘Oh, I don’t need vaccination,’ and I do want to emphasise that one of the most, if not the most effective therapy, is getting vaccinated.

But we know that nothing’s perfect, and there will be breakthroughs, or maybe some people make a personal choice that they don’t want to be vaccinated, and they can potentially get COVID-19. What we’re trying to look at is a combination therapy to see the safety and efficacy in potentially reducing the side effects and complications of COVID-19 infection. Right now, it’s a small trial, so safety and efficacy are the most important. We’ve seen in the pipelines that potential oral therapies are coming out, you’ve probably all seen it in the news. Availability of more options would be great, especially if they’re generic options, not just in the US or the UK but anywhere in the world.

Outside of the highly effective vaccines that we know are already available, the current treatments are primarily IV therapy, at least here in the US. There’s been a big push to use that, especially for people who are high risk and are not hospitalised, but we know that IV therapy can be really difficult to set up. Having a therapy where you could just go to the pharmacy, or have somebody else go to the pharmacy and pick up for you, really would improve accessibility. The key is having something that’s easy to give and easily accessible, potentially at a lower cost, too.

Technology is a big part of everything we do now. Computer technology is in almost everything. Having this rapid technology, being able to create these new interventions and bring them to the public – through potential interventions and then therapeutics – is huge.

But, technology is a double-edged sword. If I’m looking at something online, and I’m not a mechanic, I don’t know about a lot of other stuff which isn’t in my field, and I can’t separate what’s real and what’s not, it can get very confusing, because you can read something which looks like it’s coming from a reliable source. And how do you differentiate if that’s factual information versus false information? Just looking at the vaccination issues, it’s very easy to get things confused.

I think that as people use AI, and as AI technology improves over time, it can potentially even identify pandemics before they start. So maybe you can see a certain pattern or scenario in the world and identify something that’s going to happen, target it and hopefully cut off the next COVID-19 outbreak, or at least be able to contain it a lot more rapidly. That might be a wonderful use for AI just with regards to tackling future pandemics; obviously the evolution of AI is going to continue, it could help with other new innovations and therapeutics, especially with other outbreaks. This is something that’s been talked about over here, at least theoretically, but it doesn’t even have to be restricted to infectious disease: the thought of using AI for therapeutics and intervention, all sorts of medicine, is tremendous. Just extrapolating the use of AI into other spheres of humankind, for example education: it’s really just waiting for people to come up with an innovative way to use it. But I think with regards to pandemic, potentially just identifying them much earlier, that might be one way that we really utilise the technology. Within medicine, the biggest area it may be used is for new therapeutics down the road. If you’re looking at the world of endocrinology, cardiology, or cancer research, for example, the ability to identify existing and approved medications, repurpose them is vital. AI may be able to understand certain patterns and know whether these medications might actually be applicable and useful in a different area or need. That’s really where AI could help in the field of medicine.

When RUHS first heard about this potential study from our partner group, Skymount Medical, the idea of a potential oral therapy that might be much more easily accessible to the public was very appealing. Our hospital and healthcare system serves a really underrepresented and underserved area. The thought of having something that’s easily accessible to them was a huge plus, and the somewhat novel idea of using AI-based technology to be able to identify and repurpose medications was really interesting too. The thought was, if it could be used for something like infection, it could be used in other fields of medicine. That novel approach was very appealing, and that’s what got RUHS jumping on board. Obviously first and foremost is what we’re doing with this particular study, making sure it’s safe, more than anything. But the initial appeal was number one, this novel approach, and number two, being able to find a potentially accessible and affordable therapeutic for the general population, not only here but across the US and the rest of the world.