Cost-Effectiveness and Policy Implications of Personalised Medicine

Syed Sibghatullah Shah
Quaid-i-Azam University, Islamabad, Pakistan
Correspondence to: s.sibghats@eco.qau.edu.pk

DOI: https://doi.org/10.70389/PJS.100018

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Syed Sibghatullah Shah – Conceptualization, Writing – original draft, review and editing
• Guarantor: Syed Sibghatullah Shah
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: personalized medicine, cost-effectiveness analysis, pharmacogenomics, genetic testing, monte carlo simulation.

Peer Review
Received: 17 September 2024
Accepted: 30 September 2024
Published: 15 October 2024

Introduction

Precision medicine, a component of personalised medicine, is changing the face of healthcare by tailoring treatments to each patient based on their unique genetic makeup, environmental factors, and way of life.¹ The conventional “one-size-fits-all” approach to healthcare, in which treatments are made with the typical patient in mind, is giving way to a more personalised model in which interventions are fine-tuned for particular patient subgroups or individuals.² Genome sequencing, biomarker discovery, and the use of big data in healthcare have made it possible for doctors to find the best treatments, predict who will get a disease, and keep track of how well those treatments are working with a level of accuracy that has never been seen before.^3,4 Genetic information is a big part of personalised medicine because it helps doctors formulate a plan of action.⁵ Because of this, genetic profiling can help oncologists figure out what changes in tumours make them more likely to respond to certain drugs.⁶ One drug called trastuzumab is used to treat people with breast cancer who test positive for HER2.⁷ It tries to figure out how each person will break down different drugs called pharmacogenomics.⁸ This makes it possible to tailor drug treatments even more, which lowers the risk of bad drug reactions and makes treatment work better. This targeted approach might change how diseases are treated, especially ones that are hard to treat for a long time, such as diabetes, cancer, and heart disease.

Personalised medicine could be very useful, but it is not always easy to use.⁹ One of the most important things is the price, as a large amount of finances needs to be spent on research, clinical trials, and building up the skills and tools needed for genomic testing and data analysis so that personalised treatments can be used. The price of next-generation sequencing has gone down over time, but it is still pretty costly when used on whole populations.^10,11 People are also concerned about how long these new ideas will be able to work with the way healthcare is set up now because personalised therapies such as CAR-T cell treatments for some cancers are hard to understand. The cost of healthcare is going up because people are living longer and getting more chronic diseases. This is already challenging healthcare systems around the world. Policymakers who make laws and work in healthcare are worried about how much personalised medicine will cost in the long run.¹² When it comes to public healthcare systems, this is especially true because they have to balance new ideas with limited funds. Cost-effectiveness analyses (CEAs) help us figure out if the pros of personalised medicine—such as better survival rates and fewer side effects—are greater than their cons.¹³

The price of personalised medicine is not the only important issue when it comes to access and fairness. Numerous people cannot afford gene tests and personalised treatments at first. It is especially true in low- and middle-income countries where healthcare costs are low. If the right rules are not in place, personalised medicine could make healthcare even less fair, which concerns more people. It is necessary to create new laws and rules for personalised medicine to ensure that genetic information and personalised treatments are used in a good and safe way.¹⁴ But many places do not have laws that cover all of these new things. It can take longer for new treatments to be approved when old methods of controlling drugs are still in place. There also needs to be immediate legislation to safeguard the privacy of patients, mainly because genetic data is being used for more purposes. Genetic discrimination, sharing data, and getting permission should all be closely regulated so that people can trust the healthcare system.

This article provides a complete picture regarding the cost-effectiveness of personalised medicine and its effects on policy in a more general way. It is possible to add personalised medicine to healthcare systems in a way that is legal, moral, and good for business. This is the best way to get the most out of personalised medicine. In this work, we have examined the cost-effectiveness of different personalised treatments for cancer, heart disease, and rare diseases. Moreover, its influence on change costs, insurance, and patients’ health will also be explored. With personalised medicine, there are also concerns about right and wrong, especially when it comes to getting care and the chance of genetic discrimination. We have investigated the rules that are already in place and made some new ones that are fair, easy to get, and moral. These new rules will help personalised medicine grow. There are also important areas that need more research, for example, making personalised therapies that do not cost too much and getting rid of the social and financial barriers that keep people from getting these therapies. The economy needs to be looked at, rules need to be made, and everything needs to be done honestly. The purpose of this research work is to add to the ongoing conversation about personalised medicine by examining closely its policies and their cost-effectiveness. Our results can be helpful for researchers, policymakers, and people who work in healthcare.

Literature Review

Economic Impact of Personalised Medicine

In personalised medicine, care for each person will be based on their unique genetic profile, which could change how people are treated.¹⁴ This will improve the overall quality of healthcare, help patients do better, and lower the number of bad drug reactions. However, it is still not clear if these methods will increase revenue because many groups, including governments, insurers, and healthcare providers, are concerned about how much it will cost to make and use personalised treatments. We examine in detail the way through which personalised medicine might lower long-term healthcare costs, the issues that come up with high start-up costs, and whether these changes are worth the money.

Figure 1 divides the market into four product groups: personalised medical care, personalised nutrition and wellness, personalised therapeutics, and personalised diagnostics. For many years, the market as a whole has grown steadily. It went from being worth about $1,280 billion in 2015 to being worth about $2,770 billion in 2022. Personalised nutrition and wellness always had the biggest share of the market. It will grow from $810 billion in 2015 to $1,640 billion by 2022. The segment for personalised medical care also grew a lot, going from $350 billion in 2015 to $790 billion in 2022. Smaller segments, such as personalised medicine (PM) therapeutics and PM diagnostics, saw slow but steady growth in their market shares. For example, PM diagnostics went from $60 billion in 2015 to $200 billion by 2022, which is a huge increase.

Cost Reduction through Improved Outcomes and Efficiency: Personalised medicine could save money on long-term healthcare costs by making treatments more efficient and effective. Some people may need to try more than one drug before they find one that works and is well tolerated. That is because old-fashioned methods depend on trying things and seeing what works. The goal of personalised medicine, on the other hand, is to speed up treatment by using genetic and molecular data to pair patients with the treatments that are most likely to work for them.^9,16). One of the most researched areas of personalised medicine is oncology, where targeted therapies are much more effective than traditional chemotherapies. Trastuzumab (Herceptin), a drug used to treat HER2-positive breast cancer, has shown better results in the clinic than standard chemotherapy regimens.¹⁷ Trastuzumab affects the specific molecular features of the tumour, which not only increases the chance of survival but also lowers the chance of recurrence.¹⁸ This lowers the long-term costs of treating metastatic disease. Also, EGFR inhibitors such as erlotinib and gefitinib work better on certain types of non-small cell lung cancer in people with certain genetic changes, which means better outcomes and fewer side effects.

As the cost of treating serious and long-lasting illnesses increases, these kinds of improvements in how well treatments work are very helpful. Wilke et al.¹⁹ did a study that showed using genetic testing to help choose drugs can lower the number of bad drug reactions by finding people who are more likely to be toxic or not respond to certain drugs. Not managing drug reactions well not only hurts patients but also leads to more hospital stays, longer care, and higher drug costs, saving a lot of money. By cutting down on these side effects, pharmacogenomics helps keep medical costs low, and treatment pathways will save money in the long run.

Upfront Costs: Development, Testing, and Regulatory Approvals: Personalised medicine might save people money in the long run, but one of the main reasons it is not widely used is that it is very expensive to create and use these treatments. NNGS, the method used to read a person’s genome, has become cheaper over time.²⁰ It cost millions of dollars to sequence a genome in the early 2000s. Currently, it costs about $1,000. But for many patients and healthcare systems, especially in low- and middle-income countries, this price is still too high. It also costs a lot more to make personalised therapies such as CAR-T cell therapy and other advanced biologics than it does to make regular small-molecule drugs.²¹ The CAR-T therapy has worked very well for some types of leukaemia and lymphoma. When someone gets this kind of treatment, the genes of their immune cells are changed to help fight cancer. But these treatments can cost at least $1 million for each person. A lot of people cannot get them unless they have a lot of money from insurance or government health programs. This medicine is pricey because it is hard to make and needs to be tailored to each patient’s immune system. It also costs a lot to do research and development (R&D).

For personalised therapies, these costs go up even more since the government has to check them out first. For personalised treatments to work, they need tests that take into account how different are the genes of each patient. Personalised therapies, on the other hand, are not as good because they only work for a small group of people. In personalised medicine trials, on the other hand, patients are usually part of smaller groups that are closely watched. This could make it harder for other people to use the results, and it will take longer to get approval. Another thing is that the rules for personalised medicine are still being worked out, and a lot of countries do not have clear rules on how to approve these new treatments. Drug companies have to pay more because of rules that are hard to understand.

Cost-Effectiveness of Personalised Medicine Interventions: People who work in healthcare need to know if personalised medicine is worth the cost since it costs a lot to make and test. A CEA is one of the best ways to find out personalised treatment effectiveness. It is worth the money to pay for personalised therapies that help people get better and lower their long-term healthcare costs. Vellekoop et al.²² did a study that examined how cost-effective different types of personalised medicine were for a range of illnesses. It was clear that some things, such as oncology pharmacogenomic testing, were worth the money they cost. But the high prices did not cover what they provided some people, especially those with rare diseases. We can use the incremental cost-effectiveness ratio (ICER) to see if a treatment is worth the money. It was found that the ICER for trastuzumab for HER2-positive breast cancer was in the range of prices that many healthcare systems consider reasonable, especially when considering the long-term benefits of living longer and having a lower chance of recurrence. People wonder if other personalised treatments, such as CAR-T therapy, are worth the money because their ICERs are so much higher.²³ This is especially true in healthcare settings that do not have a lot of money to spend.

There are also times when personalised medicine is not worth the money. When the government runs the healthcare system, like in the United Kingdom with the National Health Service (NHS), the government has to decide whether personalised care is worth the cost to keep the system running.²⁴ Some personalised medicine programs have been started by the NHS. One of these is the 100,000 Genomes Project, which aims to make genomic testing a normal part of medical care.²⁵ Personalised medicine is not always possible in places such as the United States, where private insurance is the main way people get healthcare. It depends on how willing private insurers are to pay for genetic testing and personalised treatments. For some health problems, such as cancer and heart disease, some insurance companies now pay for pharmacogenomic testing. However, coverage is still not uniform, and many patients still have to pay for these services themselves. There are ethical concerns about fairness and access because only people who can pay for it will be able to get personalised medicine.

Balancing Innovation with Economic Constraints: The argument about whether personalised medicine is worth the money brings up a bigger problem: how can healthcare systems balance the need for new ideas with limited resources? Personalised medicine could help patients get better care and lower long-term healthcare costs, but healthcare systems are hesitant to fully adopt these technologies because they are expensive and the economic benefits are not clear.²⁶ One possible solution is to make diagnostic tools and personalised treatments more affordable. Personally tailored medicine will likely become more affordable as technologies such as NGS get better. Also, progress in artificial intelligence (AI) and machine learning could help lower the costs of analysing large genomic datasets, which would make personalised medicine even more cost-effective. Also, healthcare systems need to think about new ways to pay for therapies that take into account how they are different from other treatments. For instance, value-based pricing, in which the price of a therapy is based on how well it works clinically, might be a better way to pay for expensive personalised treatments in the long term. In this model, drug companies would be encouraged to make treatments that help patients, and healthcare systems would only pay for treatments that show they are worth the money.

Accessibility and Equity in Personalised Medicine: Accessibility and fairness are some of the most important problems that need to be solved before PM can be widely used.²⁷ Personalised medicine has a lot of potential to improve health outcomes by making treatments more effective by making them fit the needs of each patient. However, not everyone can yet benefit from it. Tests for genes, a big part of personalised medicine, are still expensive, and insurance and public health systems do not always cover them fully. This means that people with different amounts of money may not be able to get these new medical technologies.

Figure 2 shows that the world market for personalised medicine grew to $512 billion in 2022. In the next few years, the market should keep going up and reach $1,104 billion by 2032. This growth includes different areas of personalised medicine, such as nutrition, wellness, therapeutics, and diagnostics. The market keeps going up every year, but there are big jumps in the later years, especially from 2029 onwards, when it goes from $871 billion to over $1 trillion by 2031. This shows how the need for personalised healthcare solutions has grown and changed over the past 10 years.

Cost Barriers to Genetic Testing and Personalised Therapies: Gene tests are very important in personalised medicine because they allow doctors to look at a patient’s genes and figure out the best way to treat them. Due to better technology, the cost of genetic sequencing has gone down. NGS used to cost a lot of money, but now a full genome sequence costs only about $1,000. This price drop, on the other hand, is too low for many patients, especially those with low or middle incomes. Genetic tests, such as those for BRCA1/BRCA2 mutations that raise the risk of getting breast cancer, can cost a lot of money.²⁹ People who live in places with public healthcare, such as the United Kingdom, often get genetic testing paid for by their government.

Figure 3 shows that most of the people who answered (62% of those who did) were concerned that their insurance might not pay for the personalised medicine tests. Another 59% were worried about how much personalised healthcare would cost, which suggests that cost is a big reason why personalised medicine is not used more. Also, 52% of those who answered were afraid that their test results could be used to keep them from getting the treatments they want, and 51% were afraid that information about their risk of getting future diseases could be used to keep them from getting long-term care or life insurance. When healthcare is mostly run by private companies, like in the United States, things get even more complicated. A lot of insurance companies cover gene tests and personalised treatments in various ways. Prices vary based on the test, the patient’s health history, and the insurance company. Some pharmacogenomic tests are now covered by some health plans. The main goal of these tests is to find signs of heart disease or cancer. Gene tests and personalised treatments may be too expensive if we do not have good health insurance. These treatments can only be given to people who can pay for them.

Because of these differences in coverage, it is hard to get personalised medicine, especially for middle class and poor people. For example, Cheng et al.³¹ found that people from lower-income groups may have worse health outcomes if they cannot get genetic testing because of their income. This would make the difference in healthcare even bigger. Folks from poor families or living in rural areas had less access to genetic testing and personalised treatments than folks from wealthy families or living in cities. Some people are afraid that personalised medicine could make differences in health worse instead of better.

Socio-Economic Disparities in Access: People with more money might get the most advanced and individualised care, while people with less money would have to use older methods that may not work as well or may cause more side effects.³² Gene tests and targeted treatments, such as trastuzumab for HER2-positive breast cancer, were more likely to be given to people from wealthier families.^33,34 Traditional chemotherapy, which does not work as well and has more bad side effects, was more likely to be given to people from low-income families.^35,36 The writers believed that groups with less money might have worse health outcomes for cancer treatment if they cannot get personalised medicine as easily as other groups.

These differences are even bigger where they are found. It can be hard for people who live in rural or remote areas to get all the medical care they need, such as the specialised facilities needed to do genetic testing and make a personalised care plan for each person. It can be hard for people who live in the country to get personalised care because they have to travel a long way to get medical help. Also, because there is not enough healthcare infrastructure in rural areas, people who live there might not be able to take part in clinical trials for new personalised therapies. When healthcare resources are not shared fairly, it is also harder for people of colour and other groups that are not well represented to get personalised care.³⁷ Black and Hispanic women with breast cancer were not sent for genetic testing as often as White women with the same risk factors.^38,39 Different levels of income were among the causes of this difference. For example, fewer people had health insurance, and it was harder for them to get to medical facilities. There may also be implicit biases in the healthcare system that keep minority patients from getting genetic testing as often as they should.

Insurance and Reimbursement Issues: People who can get personalised medicine depend a lot on their insurance coverage. National health plans in places such as Canada and several European countries cover everyone’s healthcare costs, at least for some high-risk conditions such as cancer. Personalised medicine is often a part of these plans. National healthcare systems need to carefully weigh the costs of these cutting-edge treatments against the long-term viability of the system as a whole. Private healthcare systems, such as the one in the United States, make personalised medicine harder to get.⁴⁰ This is because insurance companies have to agree to pay for the services. Some private insurers now cover some genetic tests, but coverage is not always the same and depends on a lot of things, such as the type of test, the patient’s medical history, and how useful the insurer thinks the test is for them. In the case of cancer patients, pharmacogenomic testing is usually covered by insurance. However, genetic tests for other conditions, such as mental health disorders or heart diseases, may not be covered, so patients have to pay for them themselves.

In the United States, the Centres for Medicare & Medicaid Services has taken steps to cover some personalised medicine approaches, such as NGS for cancer patients.⁴¹ These rules are still changing, though, and coverage is still pretty limited. Also, even if the tests themselves are covered, the treatments that go with them might not be. In the case of people with melanoma, insurance might pay for a test to look for a change in the BRAF gene. People who take a targeted therapy such as vemurafenib may not get full reimbursement for their next treatment, so they have to pay a lot of money out of their own homes. Since these things happen, it is harder for people with rare diseases to get paid. Even though it would be great if personalised medicine could help treat rare genetic disorders, most of the time the medicines needed are too expensive to make and use. These medicines are also sometimes known as “orphan drugs,” which means that insurance companies might not pay for them.⁴² This makes it more difficult for people who need them to get them.

Ethical Concerns: Equity and Fairness in Personalised Medicine: We have to pay a lot of money and work hard to get personalised medicine. To make healthcare fair, everyone must be able to get the care they need, no matter what race, how much money, or where they live. The reason that it depends on pricey genetic tests and treatments is that personalised medicine might make healthcare inequality worse. It is even more important if these treatments are only for people who can pay for them. There are concerns about the fairness and justice of healthcare if there is a chance of a two-tier system where wealthy patients can get personalised medicine and poor patients must rely on standard treatments. Cancer or heart disease patients may not get the best care if they cannot get medicine that is made just for them. The United States passed the Genetic Information Nondiscrimination Act (GINA) to protect people from being denied jobs or health insurance because of their genes.⁴³ GINA does cover some types of insurance, but not life or disability insurance.

Policy Challenges and Opportunities: Healthcare systems need to be able to work well with PM. A lot of different types of genetic testing, advanced diagnostics, and targeted therapies are used in personalised medicine. Because of this, there need to be new rules, ways to pay for them, and moral supervision. Healthcare policymakers need to make big changes to the way things are done so that all patients can benefit from personalised medicine.

1.Regulatory Challenges: Fragmentation and Standardisation: The fact is that different countries do not all have the same rules for how personalised medicine works. Different parts of the world have different rules about how to approve personalised therapies such as genetic testing and targeted treatments.⁴⁴ In the old way of making drugs, therapies were only approved after being tested on a lot of patients to see how well and safely they worked. Personalised therapies, on the other hand, are made just for certain groups of patients, which makes it harder for regulators to accept them. Many times, it is tough to prove statistical significance in smaller, more specialised clinical trials for personalised therapies since there are fewer patients. As if the approval process was not hard enough, companion diagnostics make it even more difficult to find patients who will most likely benefit from a therapy. Regulatory agencies have to look over both the treatment and the diagnostic tools that are used with it.

The fact that there are no standard ways to approve these therapies around the world has led to delays and issues. For example, the Food and Drug Administration (FDA) of the United States and the European Medicines Agency (EMA) have different rules about how personalised therapies can be approved.^45,46 There are special ways for the FDA to approve personalised medicine including the Breakthrough Therapy Designation and Accelerated Approval. The goal is to speed up the approval of therapies that are a big step up from current treatments. It has taken longer for some places to change than others. People with cancer or rare diseases were asked to take part in the 100,000 Genomes Project in the United Kingdom. This was a big step towards the NHS using genomics. In the same way, Germany wants to use genomics in everyday medicine, mostly to treat cancer and other rare diseases. More people want these problems to be solved by making rules more alike all over the world. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use works to ensure that all countries have the same rules for how drugs can be used.⁴⁷ Despite this, more needs to be done to ensure that the rules for approving personalised therapies are the same everywhere. This would not only speed up the approval process, but it would also ensure that all patients around the world have the same chances to get personalised medicine.

2.Reimbursement Challenges: Bridging Innovation and Affordability: Unique treatments, such as CAR-T cell therapies or precision oncology drugs, cost a lot of money. North America, the United Kingdom, and some parts of Europe all have public healthcare. The healthcare budgets in these places are very tight to ensure that everyone gets what they need. It might save money in the long run if personalised medicine makes treatments work better and cuts down on bad drug reactions. Gene tests and personalised treatments are often too expensive for people to afford at first. People who work for public healthcare have to consider how these treatments can help people and how they can keep their jobs. This could make it take longer to get individualised care and for insurance companies to decide on payment.

CAR-T cell therapy is an example, which is a new, personalised way to treat some types of cancer that works very well but costs between $373,000 and $475,000.⁴⁸ Because of this, it is hard for healthcare systems to find the best mix of new ideas and low costs. Some countries now pay for CAR-T therapy, but it is still hard to get. There are also still doubts about how to add these expensive treatments to bigger healthcare systems in a safe way. Most of the time, private insurers would not pay for genetic tests or personalised treatments unless there is strong proof that they are worth the money. Bad drug reactions can be cut down with pharmacogenomic testing, and treatment results can get better. A lot of people cannot get the tests or treatments that could improve their health because of these insurance coverage gaps. Value-based pricing means that the price of a therapy is based on how well it works in the real world. In this model, healthcare payers would only pay for therapy if it helps people, such as by making it more likely that they will live or by shortening their time in the hospital.⁴⁹ A different solution could be risk-sharing agreements, in which drug companies and healthcare providers share the financial risk of whether or not a therapy works.⁵⁰ These deals give drug companies a reason to make treatments that help people and also help healthcare providers handle the financial risks that come with treatments that cost a lot.

3.Data Privacy and Genetic Discrimination: Ethical and Policy Considerations: Genetic information is used in personalised medicine, which raises a lot of moral and legal questions. Some of the things that come to mind are the privacy of data and the chance of genetic discrimination. It is natural for genetic information to be private, and it can tell a lot about the person being tested and their family. This is clear from the EU’s General Data Protection Regulation, which does a great job of keeping personal data, even genetic data, safe. The United States passed the GINA in 2008 to ensure that genetic information does not affect how people are treated by employers and health insurers. Genes cannot be used by employers to hire, fire, or promote people either. There are moral issues that come up with the idea of genetic discrimination that are even more important. Employers, insurance companies, or other groups could use genetic information to find people who are more likely to get certain diseases and then refuse to hire or provide services to those people.⁵¹ There might be a “genetic underclass” of people who do not get fair treatment in health insurance, the job market, or other areas of their lives because of their genes. Strong privacy protection and public education are needed to ensure that everyone knows their rights and how their genetic information is being used to fix these problems.

It would be better if the law made it clear how genetic information can be collected, used, and shared in healthcare. Also, it should be very clear how to obtain permission from a patient, genetic information should be kept as secret as possible, and people who use or share genetic information without permission should be fined. Also, they should work together to ensure that genetic information can be shared across borders for research purposes without giving out private information. Setting global standards for data privacy in personalised medicine is important so that patients do not lose trust in these new technologies.⁵²

4 Opportunities for Policy Reform and Innovation: Individualised medicine might become a part of healthcare, but many bad things could happen. The government and healthcare providers can plan and make rules that will not only make personalised medicine more available but also ensure that it stays fair and moral. For better results, it is best to create national genomic strategies. Programs such as the 100,000 Genomes Project have shown that national genomic strategies can help make personalised medicine a part of public healthcare in the United Kingdom and other places. The government could help a lot of people get personalised medicine if they built the right infrastructure, taught doctors about genomics, and made payment plans that cover genetic testing and targeted therapies. Countries such as China, Germany, and France are also working on similar projects.

Working together between the government and the private sector is another way to speed up the creation and use of personalised therapies. Researchers at colleges, big drug companies, and the government can all work together to share information, speed up clinical trials, and find new, cheap ways to pay for drugs. Shared goods and information can also help people in these partnerships deal with the money risks that come with personalised medicine. AI and machine learning are getting better, which could mean that the price of personalised medicine goes down too. A lot of genetic data can help us find patterns and guess which treatments will work best for each patient using AI. If the cost of clinical trials goes down, personalised medicine might be less expensive and also make it faster to formulate new medicines.

Methods

This article utilises CEA and cost-utility analysis (CUA) to find out the usefulness of personalised medicine interventions. Zhu et al.⁵³ and Fragoulakis et al.⁵⁴ used ICERs to explore how much pharmacogenomic-guided treatments cost per quality-adjusted life year (QALY) gained. These included policy papers and guidelines from the World Health Organisation, the National Institutes of Health, and the EMA. Moral problems were mostly those that had to do with patient consent and the privacy of genetic information. Case studies were used to look at these problems. The cost and benefit of personalised medicine interventions were evaluated to see how useful they are. The results were often shown with QALYs. To find out if pharmacogenomic-guided therapies and other personalised therapies are better for our health and worth the extra money. A lot of different treatments are tried out to find the most effective and cost-effective ones. To do this, many people keep track of how many life years they have gained. We looked at some studies that used CEA to test how useful personalised medicine was in terms of money. The ICERs are calculated as,

ICER = Cost of Personalised Medicine − Cost of Conventional Therapy Effect of Personalised Medicine − Effect of Conventional Therapy

An ICER below a certain level, which in high-income countries is usually between $50,000 and $100,000 per QALY, is usually used to measure cost-effectiveness. The ICER for targeted therapy for HER2-positive breast cancer was typical, at $62,000 per QALY. This meant that the intervention worked. We can use QALYs to help CUA figure out which patients want different health outcomes. The point of CUAs for personalised medicine interventions was to find out not only how much longer people lived but also how much better their quality of life was. In cardiology, for example, personalised treatments for arrhythmias based on genetic markers led to a big rise in QALYs because fewer people had bad drug reactions and had to go to the hospital.⁵⁵

Simulations

Besides the usual CEAs and CUAs, we also utilise simulation models that calculate the long-term costs of personalised medicine interventions. Monte Carlo simulations and other types of simulations have been used to provide personalised therapies in different care settings. It is possible to test different situations with these models. For example, they can be used to make it easier for more people to get pharmacogenomic-guided treatments or to give more genetic tests to people who are at high risk. In the statistical method of Monte Carlo simulation, random sampling and probability distributions are employed to detect model variations.^56,57 The following important steps presented in Table 1 were taken during these tests:

Case studies about genetic discrimination, genetic privacy, and informed consent were used to find the moral effects of personalised medicine. When someone applies for health insurance or a job, GINA makes sure that they are not turned down because of their genetic information. However, it does not cover things such as life or disability insurance. When it comes to informed consent in genomic research, ethics were also brought up. This is because patients need to know what sharing their genetic data will mean for them in the long run. This type of medicine is being used by more and more people, which brings up questions about who owns data, privacy, and fair access to genetic testing. This study used both a careful statistical analysis of economic evaluations and a more in-depth look at the legal and moral frameworks. It gives us a full picture of the policy and cost problems that come up when we try to add personalised medicine to the healthcare system.

Results

The comprehensive study we carried out paints a complete picture of the various financial pros and cons of personalised medicine. In addition, it finds gaps in policies, rules, and concerns that come up when personalised therapies are used in healthcare systems. Our research showed that personalised medicine can save a lot of money in the long run, especially in areas where precise and targeted treatments can greatly cut down on the waste that comes with traditional “one-size-fits-all” methods. Personalised medicine improves treatment outcomes by adapting interventions to each patient’s genetic profile. This makes therapy work better and lowers the chance of side effects. But the high costs of genetic testing, making new drugs, and building infrastructure are still big issues that stop a lot of people from using it.

One of the most obvious ways that personalised medicine can help the economy is by making longer-term, more difficult diseases such as cancer easier to treat. This can lower the cost of healthcare. For example, pharmacogenomic testing is cost-effective in some groups of people with cancer. People use trastuzumab (Herceptin) as an example, which is a targeted treatment for HER2-positive breast cancer that works better than regular chemotherapy. Trastuzumab saves money because it lowers the number of treatments that need to be tried and failed. Targeted therapies for cystic fibrosis and other rare genetic diseases have also been shown to be cost-effective because they treat the genetic mutations that cause these conditions directly. This makes things better for patients and lowers the cost of long-term care.

Barriers to Widespread Adoption: High Upfront Costs

One big reason more people do not use personalised medicine is because of higher costs. However, it saves money in the long run. A lot of money needs to be spent on research, clinical trials, and building up the infrastructure needed for genetic testing, biomarker discovery, and personalised therapies to be possible. NGS is important for finding genetic markers that help doctors tailor treatments to each person’s needs. The cost of NGS has gone down from millions of dollars to around $1,000 per genome. This is still too high, especially for countries with low or middle incomes that do not have a lot of money for healthcare. Personalised medicine’s cost-effectiveness depends a lot on the healthcare system, the disease being treated, and the resources that are available to help integrate personalised treatments (Table 2).

Policy and Regulatory Gaps

There are gaps in the regulatory frameworks and reimbursement models that make it hard to add personalised medicine to healthcare systems. Our review found several important areas where current healthcare policies fail to support the use of personalised therapies. These include not ensuring equal access and not creating long-term reimbursement models. There are not enough complete reimbursement plans for genomic testing and personalised therapies, which is one of the main problems. In the United States, private insurance companies have started to pay for some genetic tests. These tests are mostly used to find genetic causes of cancer and heart disease. On the other hand, many developing countries’ public healthcare systems have trouble planning for how much these tests will cost.

Medicare and Medicaid in the United States have taken steps to pay for people with cancer to get next-generation sequencing. It is very different to get genetic tests and personalised treatments in low- and middle-income countries, where there are no ways to pay for them. People with rare diseases often cannot afford individualised treatments unless they get a lot of help from their insurance or the government. Another big issue is that personalised medicine is governed by various rules. The rules in many countries do not make it clear how to approve therapies that are based on people’s genetic profiles. Due to the absence of standards, it is harder to get personalised treatments on the market, and approval times can get pushed back. For example, in the United States, the FDA has set up ways to speed up the approval of personalised therapies that are much better than current ones. Some of these are Accelerated Approval and Breakthrough Therapy Designation. This makes it hard to get personalised care and takes longer than it should. Also, getting companion diagnostics approved by regulators is still a big problem (Table 3).

Ethical and Equity Considerations

Personalised medicine comes with a lot of problems, including genetic privacy, the possibility of discrimination, and unequal access to new therapies. As personalised medicine becomes more common in healthcare, our review brought up some ethical issues that policymakers need to address. Making sure that genetic data is kept private is one of the most important moral issues. In the United States, laws such as the GINA are meant to stop discrimination based on genetic information in health insurance and jobs. However, there are still some things that are not clear, such as how life and disability insurance companies use genetic data.

People may also not want to get genetic testing if they are concerned about what will happen if other people use their information in the future. Another big ethical problem is that people from different social classes do not always have the same access to personalised medicine. According to our work, personalised therapies and genetic testing are usually easier to get for wealthy people who can pay for them out of pocket. This is especially true in countries where insurance coverage is not always consistent. There is a chance that this will lead to a two-tiered healthcare system where patients with more income get more advanced and personalised care while patients with less income have to rely on more traditional treatments that may not work as well. We need policies right away that ensure everyone has the same chance to get personalised medicine to solve these problems. This means coming up with cheaper ways for disadvantaged groups to get genetic testing and personalised treatments. Some ideas are to make subsidies, change how insurance works, and create public-private partnerships (Table 4). Life years gained or QALYs are common units used to show figures. The CEA is a key part of personalised medicine. It helps doctors decide if the health benefits of targeted therapies are worth the high costs. The goal of these studies, which looked at heart disease and cancer, was to find the most cost-effective treatments by comparing pharmacogenomic-guided treatments to standard therapies.

There is a need to figure out how much pharmacogenomics-based cancer treatments cost per quality-adjusted life year gained. To find the ICER, divide the difference between how much two interventions cost by how well they work: if the ICER is below a certain level, then it is regarded as good. Most of the time, this level is between $50,000 and $100,000 per QALY in wealthy countries. The ICER for targeted therapy trastuzumab in women with HER2-positive breast cancer was $62,000 per QALY.⁵⁸ Therefore, trastuzumab is a good and inexpensive way to help women with HER2-positive breast cancer. The study showed that trastuzumab has high costs at first, but the long-term health benefits, such as higher survival rates and fewer recurrences, outweigh the costs. Personalised medicine can save a lot of money, especially when targeted therapies make a big difference. But cost-effectiveness depends on a lot of things, such as the disease, the available infrastructure, and how things are going in the healthcare system. Therefore, there is enormous importance of having personalised health economic plans to make these treatments available and last.

Simulation Results

Monte Carlo simulations were used to figure out the ways through which personalised medicine would change the long-term costs of healthcare systems. In these simulations, random sampling and probability distributions were used to model things such as costs, clinical outcomes (QALYs), bad drug reactions, survival rates, and hospital stays. It took us a few tries to ensure that our estimates of cost-effectiveness were correct in a variety of situations, such as when we increased genetic testing for people at high risk and pharmacogenomic testing for people with heart disease. It costs $58,500 per QALY to test more people with a high risk of cancer genetically presented in Table 5. This number is a lot less than the usual cost-effectiveness threshold of $100,000 per QALY. There is a 75% chance that this scenario is cost-effective, which means that expanding genetic testing would probably be good for the economy. This is because it would make treatments more accurate and lower bad outcomes such as cancer recurrence. Due to genetic testing, targeted treatments are now possible for more patients, which is shown by the rise in QALYs. The big difference in incremental costs between patients shows how expensive it can be to treat different people and the role of targeted therapies in saving money (Table 6).

The ICER for pharmacogenomic testing in people with cardiovascular disease is $42,000 per QALY. This is a lot less than the $100,000 threshold. There is a strong economic case for using pharmacogenomic testing because there is an 88% chance that this intervention will be worth the money. This is especially true when you think about how much better it is for patients to avoid bad drug reactions and hospital stays. The extra QALYs (1.5 QALYs) show that pharmacogenomic testing not only makes treatment cheaper but also improves patients’ quality of life in a big way. Because of this, pharmacogenomic testing is a very good way to treat cardiovascular disease.

The ICER for expanding personalised immunotherapies for cancer is $115,000 per QALY as presented in Table 7, which is more than the $100,000 level. There are clear clinical benefits to this intervention, but it is not as cost-effective as other personalised medicine approaches because it costs a lot. This scenario shows that current immunotherapy costs are too high to consistently meet cost-effectiveness criteria, with a 40% chance of being cost-effective. But lowering the costs upfront might make this happen better in the long run. Personalised cancer immunotherapies have big clinical benefits (0.8 QALYs), but they are hard for many people to get because they are so expensive. The high standard deviation shows that costs vary a lot, which means that therapy development and delivery need to be improved even more to get better economic results.

The simulations showed us a lot about how personalised medicine would affect the economy and patients with a variety of diseases. The results of the Monte Carlo simulations are shown in Table 8. The Monte Carlo simulations back up the results from the CEAs and CUAs, giving us a complete picture of how personalised medicine affects the economy. The simulations showed that it is very cost-effective to do more genetic testing on groups of people who are at high risk for some cancers, such as those with hereditary breast and ovarian cancer syndromes. With an average ICER of $58,500 per QALY and a 75% chance of falling below the $100,000 mark, this intervention makes a strong case for healthcare systems to put money into it. A big part of the cost savings comes from not having to try different treatments and having fewer cancer recurrences.

The simulations for people with cardiovascular disease who were getting pharmacogenomic testing showed even better cost-effectiveness, with an average ICER of $42,000 per QALY and an 88% chance of being cost-effective. The drop in bad drug reactions and hospitalisations led to this result. A big chunk of healthcare costs comes from cardiovascular diseases. Using pharmacogenomics to guide therapy has been shown to stop expensive side effects, making it a very good idea from both a clinical and an economic point of view. The results were not as good for expanding personalised cancer immunotherapies. The mean ICER was $115,000 per QALY, and there was only a 40% chance that the strategy would be cost-effective. This is mostly because creating and using immunotherapies such as CAR-T cells still costs a lot of money upfront, making them out of reach for many healthcare systems. However, these therapies often have big effects on patients’ chances of survival, and more research into how to make them cheaper could make them more cost-effective over time.

Figure 4 shows a scatterplot with different outcomes for different simulations. Each dot shows a different scenario of increasing genetic testing in high-risk cancer populations, how much it would cost, and how many quality-adjusted life years it would save. The price level at which people are willing to pay $100,000 per QALY is shown by a diagonal line. Points below this line show outcomes that are good value for money. The high number of points below the threshold suggests that genetic testing on a larger scale in this population is likely to be cost-effective.

The relationship between the costs and benefits of pharmacogenomic testing in people with cardiovascular disease is shown in Figure 5’s cost-effectiveness plane. The results show that pharmacogenomic testing for cardiovascular conditions is financially viable, with a high chance of lowering hospitalisations and improving patient outcomes. This is supported by the fact that most of the simulated points fall within the cost-effective quadrant. There is strong evidence from the Monte Carlo simulations that personalised medicine interventions are cost-effective in high-risk groups that are specifically targeted. Both increasing genetic testing for cancer and pharmacogenomic testing in heart patients have a good chance of making things better and lowering long-term healthcare costs. The simulations show that we need to keep working to lower costs and make personalised medicine more available. They also show us where more research and strategies for keeping costs down are most needed right away.

Discussion

Personalised medicine is quickly becoming known as a revolutionary way to change healthcare. It could make treatment much more effective while also lowering long-term healthcare costs. Personalised medicine has worked to improve drug reactions, make treatments more effective, and treat diseases that previously did not have effective treatments. It does this by using a person’s genetic information to make therapies more effective. Our findings show that personalised medicine has a lot of potential to save money in the long run, especially when it comes to cancer and rare genetic diseases. The findings show that treatments such as trastuzumab for breast cancer that are positive for HER2 are very good value for money. With an ICER of $62,000 per QALY, these treatments are thought to be worth the money in countries with high incomes. The results of this study agree with some other studies that also found that precision medicine in oncology is more cost-effective than traditional treatments.^44,59,60

It will save money in the long run, but the costs of making and using personalised medicine are still too high right now. As we saw, genetic testing such as NGS is still very expensive, and a lot of healthcare systems, especially in low- and middle-income countries, find it hard to use these technologies as standard. These results are in line with a meta-analysis by Rose⁶¹ and Sturdy⁶² that also pointed out that the high start-up costs of personalised medicine interventions make them less likely to be used by more people. In the same way, Cutler⁶³ pointed out that the high costs of drug development, clinical trials, and regulatory processes make precision medicine even harder to afford. Interestingly, targeted therapies such as CAR-T cell therapy have shown a lot of clinical benefits. However, our results show that their ICER values ($373,000–$475,000 per QALY) are well above the usual cost-effectiveness thresholds, which makes us wonder if they will be able to keep working in the long term. Giorgioni et al.⁶⁴ have found high costs of advanced biologics, such as CAR-T therapies, make it hard for healthcare systems to use them on a large scale. To make personalised medicine more financially viable in the future, it will be important to find ways to lower the costs of genetic testing and personalised therapies through better technology and more competition in the biotechnology field.

Our work shows that there are gaps in the regulatory frameworks and reimbursement models that make it hard for personalised medicine to be used by many people. In many countries, especially those with public healthcare systems, it is hard to make rules about who pays for expensive genetic tests and targeted therapies. Some private insurers in the United States have started to cover certain genetic tests, but our research shows that many public healthcare systems, especially in developing countries, do not have the money or the right infrastructure to support these expensive changes. Public-private partnerships and government spending on genomic infrastructure have been shown to help personalised medicine take off in a way that keeps costs low and brings about new ideas. It was said that the UK’s model was the best way to use genomics in healthcare, especially when it came to making sure that patients are reimbursed and that rules were standardised. Not having standardised regulatory pathways for personalised therapies, on the other hand, means that patients in those countries have to wait longer for therapies to be approved and cannot get them as often. Both the FDA and the EMA have begun working on paths such as Adaptive Pathways and Breakthrough Therapy Designation. However, there needs to be more harmonisation of rules around the world so that personalised therapies are easier to approve and can be used in all healthcare systems. Vellekoop et al.²² also said that rules that make it hard to use personalised medicine need to be fixed by making global rules for the approval of diagnostics and treatments that work together.

Fee-for-service models do not work well for treatments such as CAR-T therapy that only need to be paid for once and cost a lot of money upfront. Long-term, value-based pricing models may be better. In these models, the cost of a therapy is based on how well it works in the clinic. Personalised medicine might also be easier to get if healthcare payers and drug companies agree to share risks. It is agreed upon that the financial risks will be split if treatments do not work as planned. Building strong frameworks for data governance is becoming more imperative. To ensure that genetic data is used responsibly and openly, there needs to be stronger international rules on data privacy. Changes in genetic testing and targeted therapies based on income could make healthcare inequality worse. Gene testing is expensive and hard to get for people from low-income and racial or ethnic minority groups. For this reason, lawmakers need to work together to create laws that give everyone, irrespective of wealth, an equal opportunity to receive personalised medicine. To assist groups that do not get enough help paying for genetic tests and treatments, this includes providing them with subsidies, making insurance more accessible, and encouraging public-private partnerships. People in low- and middle-income countries have a harder time getting these cutting-edge treatments because of the lack of infrastructure. Our research, on the other hand, shows that we need global solutions that take into account the specific problems that developing healthcare systems face.

Limitations of the Study

The fact that this study is based on old research and simulation models is a big problem because they may not completely demonstrate the complexity of current healthcare systems. We can get a good idea of cost-effectiveness from Monte Carlo simulations, but they are based on assumptions that are not always true about costs, clinical outcomes, or healthcare use. These assumptions can be very different for different people and places. There is more personalised medicine in high-income countries. Because low- and middle-income countries have different healthcare systems and fewer financial resources, the results may not be valuable there. Finally, the study did not examine how quickly personalised medicine technologies are improving, which could soon affect costs and outcomes.

Conclusion

People who use personalised medicine will be able to get treatments that are just right for them. This can make a big difference in their effectiveness and lower long-term costs. But it cannot reach its full potential because of complicated moral, legal, and financial issues that need everyone in the healthcare system to work together. To ensure that personalised medicine becomes a normal part of care, healthcare policymakers and other interested parties must focus on creating fair, cost-effective, and long-lasting frameworks that support the clinical and economic viability of these new ideas. The high costs of genomic testing and targeted therapies at the start are a big problem, especially in places with few resources. In the future, people should work on making these technologies available to more people by lowering their costs through new technologies and changes to the rules that encourage innovation without putting patient safety or access at risk. Also, protecting genetic information and stopping discrimination must stay at the top of this transition. This is the only way to ensure that patients can trust the systems that are meant to keep their most private information safe.

As personalised medicine develops further, it will be important for people all over the world to work together and be open to new ideas. Personalised medicine can truly change healthcare for everyone by making it easier to get and more affordable, especially in developing countries. This way, the benefits of precision therapies will not just go to a few but will spread to everyone, making the healthcare system around the world more fair and effective. For a better understanding of whether personalised medicine is possible around the world, future research should focus on expanding cost-effectiveness analyses to include a wider range of healthcare systems, especially in low- and middle-income countries. In addition, it will be important to reevaluate the economic impact of technologies such as NGS as they continue to improve and become more accessible in realistic clinical settings. Beyond cost-effectiveness, research should also look into how personalised therapies affect patients’ quality of life and happiness over the long term. Also, more work should focus on creating new reimbursement models and policy frameworks that can help personalised medicine become a fair part of public healthcare systems, protecting everyone’s access.

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Cite this article as:
Shah SS. Cost-Effectiveness and Policy Implications of Personalised Medicine. Premier Journal of Science 2024:2;100018

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