Are We Ready for the Century of Biology?

As technology advances every day, we find ourselves in an entirely new era. We see this progress in every part of life: vaccines made familiar by the pandemic, drugs with fewer side effects, earlier cancer diagnoses, improved imaging, genetic research and personalized precision treatments. Countless developments like these have led our century to be called the century of biology. But what does the century of biology actually mean, and are we ready for it?  

The 20th century is regarded as the century of physics and technology. Numerous inventions in these fields took the world to an entirely new level. Figures such as Albert Einstein and Steve Jobs secured their places in world history. With the beginning of the 2000s and advances in biotechnology , we entered a new era. 

The Beginning of the Century of Biology

Launched in 1990 and completed 13 years later in April 2003, the Human Genome Project is one of the most important studies in human history. It was a turning point for us and perhaps the beginning of the century of biology. 

The Human Genome Project mapped the entire human genome. This gave us a better understanding of gene structure and function, in other words, how genes work. The new knowledge brought hope for many genetic diseases, including cancer, diabetes, lupus and Alzheimer's. Research in these areas expanded, and genetic studies accelerated. Gene-editing technologies have now enabled new approaches to genetic diseases and the development of new drugs. The best-known of these technologies, which also won the Nobel Prize in Medicine in 2020, is CRISPR. 

CRISPR (Clustered Regularly Interspaced Palindromic Repeats, with the same meaning expressed by its Turkish translation) is essentially a technology that allows us to edit genes. We can use it to silence, edit or change a gene. This technology, which has gained an important place in treating diseases such as cancer, hepatitis B and sickle cell anemia, has already begun to demonstrate benefits.

Jimi Olaghere's life was extremely difficult because he had sickle cell anemia, an inherited disease that slows or blocks blood flow and causes painful symptoms. His symptoms frequently forced him to cancel flights. Repeatedly admitted to intensive care, Jimi had to choose a different career path because of his crises. He once described his illness as follows: “Sickle cell anemia feels like a puppet master controlling my life, and I am its puppet.” 

Born and raised in Nigeria, Jimi went to America hoping for treatment. There, through a clinical trial, he was told about a treatment that would help him lead a normal life and enjoy experiences such as parenthood. 

For this treatment, scientists removed cells from Jimi's blood and edited his DNA using CRISPR. He then received a drug to clear his bone marrow (the site where blood cells are produced) . After an intensive regimen, the new cells were infused into Jimi's blood. They began producing healthy cells, and his need for transfusions (blood transfusions) was almost eliminated. 

Many more people like Jimi Olaghere can regain their health and live happy lives through new treatments made possible by advancing technology. But are we keeping pace with the century of biology?

Pushing the Limits of Medicine

We have discussed the benefits of revolutionary discoveries such as the Human Genome Project and CRISPR. Yet these do not allow us to overlook the challenges of this era. 

The past 10 years are described as a golden age of drug discovery . From 2012 to 2021, 430 new drugs received FDA (US Food and Drug Administration) approval. Compared with the previous 10 years, that was 73% more. These 430 approved drugs included cancer immunotherapies, diabetes drugs to combat obesity, meningitis vaccines and Covid vaccines, as well as the first gene therapies. 

Image: Number of drugs approved annually by the FDA

By the end of this era, medicine's limits were beginning to be stretched, and progress began to slow. For example, rather than further technical improvements to CRISPR, testing will now focus on how well it works as a treatment. Effectiveness is not the only consideration for a treatment or drug; safety matters too. For instance, the arthritis (joint inflammation) drug that broke sales records in 2004, Vioxx, was later withdrawn after clinical trials showed that it caused heart attacks. Amid the resulting concerns about trust, many more drugs were found to cause other diseases. Such controversies are less common today, but not because a safe system has been established. Instead, scientists have taken a very different path. 

New Strategies for New Drugs

Developing a new drug and bringing it to market is extremely difficult. Laboratory experiments, clinical trials, randomized groups of human volunteers (with patients allocated at random) and placebo groups, together with the collection of the necessary data, make the process lengthy and expensive. Despite all this research, every chemical treatment is known to have side effects. Here, the main goal is for the drug to do more good than harm.

To prove that a drug works, it is given appropriately to thousands of people, and side effects are observed. This is the only way scientists can be sure about a drug. Over time, however, this approach increased participant numbers and the cost per patient. In 2015, a pharmaceutical company announced that a large trial of 30 thousand people for a new cholesterol-lowering treatment had cost more than $1 billion. This cost problem severely limited research. 

Rather than confronting the difficulty of gathering data, pharmaceutical companies began focusing on rarer conditions such as multiple sclerosis (MS), less common cancers and autoimmune diseases. Results from drugs can be obtained much faster in these conditions than in common diseases. For a cancer patient with no remaining hope, for example, even tumor shrinkage is considered a success. This allowed companies to develop drugs in much smaller study populations and market them at higher prices.

Between 2007 and 2021, the average monthly price of a new drug rose from $2115 to $180 thousand. The proportion of drugs costing more than $150 thousand also increased from 9% to 47%. Many scientists believe this approach is wrong compared with trials using randomly assigned groups, because it makes it very difficult to determine whether a drug really works. Yet medicine lacks adequate infrastructure for a proper system of randomized trials. This leaves us unprepared for the century of biology . There are many promising drugs, but conducting trials properly has become extremely expensive, whether the disease is rare or common.

Another problem is deciding which trials should be funded. Funding every promising clinical study is not necessarily a useful solution. For example, some pharmaceutical giants began developing every possible combination of immunotherapy drugs. For his work developing immunotherapy drugs in this field, James Allison received a Nobel Prize, and he says this approach is completely wrong. What is needed, he says, is a system for conducting the right small studies and collecting patient data to understand how the immune system responds to each new drug combination. Allison says any trial that does not meet these needs is misguided because it will fail. To conduct the right studies outside the existing system, he established the James P. Allison Institute.

Using the Covid-19 Pandemic Crisis as an Opportunity

Amid these difficulties, an unexpected event provided an opportunity to conduct research within a proper system. Researchers carried out highly productive studies during the pandemic crisis. Vaccine developers, for example, could quickly study a group of 30 thousand volunteers and collect data by observing patients. The crisis allowed large, effective studies like those conducted 20 years earlier, bringing vaccines from different companies to market within just a few months. 

During the pandemic, the development of vaccines and drugs intended to treat people who were already ill exposed all the problems in the system. 

At the beginning of the Covid pandemic, drugs initially proposed for treatment included hydroxychloroquine, ivermectin and tocilizumab . Rather than properly testing whether these drugs worked, thousands of small studies were launched that could not determine whether they helped patients recover. Meanwhile, two academics in England changed the course of the pandemic: Martin Landray and Peter Horby. 

Landray argued for a large clinical trial to establish which drugs could treat the disease. Through an intermediary, he later met Horby, an infectious disease epidemiologist at Oxford. They realized they could use the UK National Health Service to carry out the necessary research. Clinical trials normally try to track every piece of data, driving up costs. Instead, Landray and Horby focused on information readily available in medical records and carefully extracted the data they needed. Their study received national approval from the NHS (UK National Health Service), allowing ordinary doctors, rather than only those working with pharmaceutical companies, to participate. 

Landray and Horby's study, RECOVERY, was more decisive than other research in identifying drugs that could help patients with Covid. It showed that hydroxychloroquine (Plaquenil), widely used in New York, was ineffective. An inexpensive drug used instead 2 weeks later, dexamethasone (cortisone), reduced mortality among ventilated patients by 35%. The study produced many other positive findings, and Landray and Horby were knighted in England. 

This was not the only effective approach. Another was the use of real-world evidence: data collected from health records and sources outside clinical trials. These data come from many places, including routine patient visits, pharmacy records and even social media. Using them to monitor drug effectiveness and side effects proved highly effective during the pandemic. 

After the lessons of the pandemic, people began asking why collecting information on whether treatments work, or how diseases develop and spread, should be solely the task of research. Ideally, it should be a feature of the healthcare system itself. An ideal system automatically collects data and conducts randomized controlled trials of different treatments. Why no such system exists is harder to answer than it appears. Different perspectives make the process even more difficult. For example, the US approach to healthcare emphasizes inventing new devices. Instead of seeking faster, more effective methods and improving infrastructure, it focuses on new treatments. This can overlook treatments capable of benefiting large populations while producing very expensive drugs that people cannot access.    

What Is the Right Research Approach?

Finding the right system is complex. A treatment's journey from research to market is lengthy and difficult, and the work does not end once it is marketed: observations continue for years to understand its effects. Questions remain about how to optimize this entire process. The first crossroads is whether improving clinical trial infrastructure or pursuing real-world evidence would be more effective. 

The idea of conducting clinical trials quickly and effectively is not new. Early trials could produce effective results, much like Landray and Horby's work during Covid. Over the years, trials grew larger, but data collection became expensive. Companies are willing to pay to collect data on a blockbuster drug. This means large studies will be conducted only for drugs they consider potentially profitable. 

FDA official Robert Califf says simple trials are needed so that data can be collected somehow. On becoming head of the FDA for the second time, he made the need for better data systems and higher standards central to his speeches. Whether the FDA has enough power to create this system is unclear. Califf is looking elsewhere for a solution. 

Verily is the life sciences research arm of an American technology company. Califf is working with it to find solutions for conducting trials and collecting data. Verily is led by former FDA official Amy Abernethy, who says she wants to create studies that follow patients and collect data seamlessly. Technological progress will make these studies cheaper, enabling randomized trials that can collect more data (studies that randomly select people and can produce more accurate results) . Abernethy believes technology can reduce the size of placebo groups and accelerate research. Even with this technology, however, she says randomized trials will continue and random selection must be conducted very carefully and systematically. 

It is encouraging that Verily plans randomized controlled trials as well as data collection. Many real-world evidence companies already exist, providing unique data that allow simulations before clinical trials. This can prevent major, costly failures and show doctors how real life differs from research. With pressure from pharmaceutical companies, real-world evidence is being used instead of clinical trials. Although Congress supports this in the US, there is anger that research into some rare diseases is still not progressing quickly enough. Congress considers real-world evidence more effective than randomized trials. A group of bioethicists disagrees. In a letter, they said drugs without sufficient evidence of safety and effectiveness would not benefit patients, that Congress was simply trying to make the FDA approve drugs faster, and that the FDA appeared willing to comply. They also said research so far had shown that real-world evidence did not work. The billion-dollar budget allocated to clinical studies is currently going toward early ideas that could not otherwise obtain funding, rather than addressing concerns about trust or producing more robust results. 

What Does Technology Offer Cancer Research?

Finally, the main focus in cancer vaccine research is not on clinical trials. This risks spending too much money on the wrong area and ultimately failing. Research without the right data is highly risky. 

Another example is multicancer detection tests, which aim to detect cancer early by sequencing DNA fragments in the blood. Even when they succeed, some critics remain uncertain whether early detection will help: it is unclear whether the cancer detected would ever cause a problem or might disappear on its own. 

Wanting to get a treatment to patients quickly is entirely natural. Yet it is essential to do so properly without skipping any evaluation. Applying an unclear system to conditions ranging from rare to common diseases will cause problems. What matters is establishing a sound research system. CAR-T technology, a recent effective development, offers an example. 

CAR-T (chimeric antigen receptor-modified T-cell therapy) involves removing a patient's own T cells. With the help of a virus, the cells are genetically modified to attack cancer cells. 

CAR-T was first approved in 2017 for children and adults with no other options. It took 5 years to demonstrate that the technology was more effective than bone marrow for patients receiving second-line treatment. 

Douglas Olson was one of the first cancer patients treated with CAR-T, 14 years ago. He had chronic lymphocytic leukemia (CLL). He realized the technology used to treat him was still not approved for adults with CLL because it had proved less effective than in some other blood cancers. As new drugs entered the market, CLL studies became less of a priority. Olson participated in a safety trial, and the treatment worked for him. But he says he would not receive CAR-T if he had CLL today, and he does not understand why. This shows how important the right data are. 

What Do We Ultimately Need?

We live in a century of biology, with new treatments and groundbreaking discoveries emerging every day. Yet the current system has infrastructure problems and many other shortcomings. To accelerate progress and bring new treatments to market safely, we need a system that provides the right data and enables effective clinical studies. If we achieve this, exciting days lie ahead for healthcare.