After the High Court intervened, CM Yogi issued the following directive in Lucknow: “No hospital will return the patient; if there is no bed in the government hospital, the patient will be sent to the private hospital, and the state government will bear the entire cost.” Navneet Sehgal ACS Information

If the responsible people of your town do not answer the phone, dial these numbers directly and request assistance since someone immediately needs to respond:-

 All District Magistrates have been added to WhatsApp as per the Uttar Pradesh High Court’s directive. Disputes can be made directly on DMs  WhatsApp. Contact details of District Magistrates of all districts of Uttar Pradesh.

Reference:-

• Ministry of Health & Family Welfare – https://main.mohfw.gov.in/
• Medical Health and Family Welfare Department, Uttar Pradesh-http://www.up-health.in/

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The tweet, which garnered significant attention on social media, highlighted the critical issue of access to essential medicines for cancer patients. Cancer is a deadly disease that requires immediate treatment, and any delay in accessing the required medication can significantly impact a patient’s chances of survival.

The PMO responded promptly to the tweet, assuring the sister that the matter would be looked into and necessary actions would be taken. This is a positive step towards addressing the issue of access to cancer medication, and it highlights the importance of using social media to bring attention to critical issues that require immediate action.

Cancer is a disease that affects millions of people worldwide and requires specialized treatment and medication. Unfortunately, due to the high cost of cancer drugs, many patients are unable to afford the required medication, which puts their lives at risk.

It is crucial for governments to recognize the importance of providing affordable access to essential medicines for cancer patients. The availability of generic drugs can significantly reduce the cost of cancer treatment, making it more accessible to those who need it.

Moreover, it is imperative to ensure that the healthcare system has the necessary infrastructure and resources to provide quality cancer care. This includes investing in specialized cancer treatment centers, training healthcare professionals, and providing patients with access to the latest diagnostic and treatment technologies.

In conclusion, the tweet by Shubhash Hudda’s sister highlights the critical issue of access to essential medicines for cancer patients. The response from the PMO is a positive step towards addressing this issue, and it underscores the importance of using social media to bring attention to critical issues that require immediate action. It is essential for governments to recognize the importance of providing affordable access to essential medicines for cancer patients and invest in the necessary infrastructure and resources to provide quality cancer care.

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Bioinformatics Goals

To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include:
• Development and implementation of computer programs that enable efficient access to, management and use of, various types of information.
• Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences.
The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis.
Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.

Bioinformatics Tool & Analysis

Tools of a bioinformatics expert are computer software programs and the internet. The main activity is sequence analysis of DNA and proteins using various programs and databases available on the world wide web. Bioinformatics expert from clinicians to molecular biologists, with access to the internet and relevant websites can now freely discover the composition of biological molecules such as nucleic acids and proteins by using basic bioinformatics tools for retrieving, sorting out, analyzing, predicting, and storing DNA and protein sequence data.
The growth of bioinformatics has been a global venture, creating computer networks that have allowed easy access to biological data and enabled the development of software programs for effortless analysis. Multiple international projects aimed at providing gene and protein databases are available freely to the whole scientific community via the internet.

Useful bioinformatics websites (available freely on the internet)

Bioinformatics Applications in Life science & Health care

Bioinformatics has proven quite useful in medicine as the complete sequencing of the human genome has helped to unlock the genetic contribution for many diseases. Its applications include drug discovery, personalized medicine, preventative medicine and gene therapy.

Drug Discovery
The incorporation of bioinformatics into the drug discovery and development industry is often referred to as translational bioinformatics. Despite the medical advancements and discoveries that have revolutionized how diseases are diagnosed and treated over the past several decades, researchers estimate that only approximately 30% of all known diseases can be treated with currently available pharmacological agents. This gap in the availability of tested and approved therapies is further expanded by the fact that many biological targets have yet to be identified for many communicable and non-communicable diseases.
In addition to a lack of treatment options for known diseases, the emergence of novel viruses like the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) further emphasizes the need to improve the rational drug design process.
Advancing the tools available for translational bioinformatics is one potential resolution to this growing problem. With its incorporation into almost every phase of the drug discovery process beginning with preclinical research on novel drugs and continuing throughout clinical trials and post-launch, translational bioinformatics can undoubtedly assist in the discovery of effective drugs for both communicable and non-communicable diseases.

Personalized Medicine
Medicine is experiencing a period of change: Extensive molecular biological data on the patient are increasingly included in diagnosis and treatment. This trend is based on the development of targeted drugs and accompanying diagnostics, which serve the purpose of providing advance evidence that the medication promises therapy success for the patient. According to this concept drugs are often given in combination. The sizes of patient groups for which a given therapy out of many possible alternatives can be expected to be successful are quite limited. The relationship between the molecular data pertaining to a patient and their disease phenotype are complex and cannot be determined manually. Thus, computer-based bioinformatics methods play a central role in interpreting the molecular data and as an instrument for providing recommendations for the practicing physician. Bioinformatics is an essential component in basic research, in the development of new concepts for diagnosis and therapy as well as in clinical practice, in which these concepts are applied to treating patients.
In the last decade, molecular science has made many advances to benefit medicine, including the Human Genome project, International HapMap project and genome wide association studies (GWASs) (International HapMap Consortium, 2005). Single nucleotide polymorphisms (SNPs) are now recognized as the main cause of human genetic variability and are already a valuable resource for mapping complex genetic traits. Thousands of DNA variants have been identified that are associated with diseases and traits. By combining these genetic associations with phenotypes and drug response, personalized medicine will tailor treatments to the patients’ specific genotype.

Preventative Medicine
Preventive medicine focuses on the health of individuals, communities and defined populations. It uses various research methods, including biostatistics, bioinformatics and epidemiology, to understand the patterns and the causes of health and disease, and to transform such information into programs designed to prevent disease, disability and death.
With the specific details of the genetic mechanisms of diseases being unraveled, the development of diagnostic tests to measure a persons susceptibility to different diseases may become a distinct reality. Preventative actions such as change of lifestyle or having treatment at the earliest possible stages when they are more likely to be successful, could result in huge advances in our struggle to conquer disease.
An example of preventive medicine is the screening of newborns immediately after birth for health disorders, such genetic diseases or metabolic disorders that are treatable but not clinically evident in the newborn period.
To develop such screening tests to identify the disease at an early stage, researchers use bioinformatics tools to analyze genomics, proteomics and metabolomics data for possible disease biomarkers.

Gene therapy
Gene therapy involves repair or replacement of mutated genes. Gene expression can also be altered in order to regulate the immune system. Gene therapy requires, characterization of defective gene, use of vector system for insertion of gene and recombination between defective and functional gene.
Gene therapy research has expanded from its original concept of replacing absent or defective DNA with functional DNA for transcription. Genetic material may be delivered via multiple vectors, including naked plasmid DNA, viruses and even cells with the goal of increasing gene expression; and the targeting of specific tissues or cell types is aimed at decreasing risks of systemic or side effects. As with the development of any drug, there is an amount of empiricism in the choice of gene target, route of administration, dosing and in particular the scaling-up from pre-clinical models to clinical trials. Systems Biology, whose arsenal includes high-throughput experimental and computational studies that account for the complexities of host-disease-therapy interactions, holds significant promise in aiding the development and optimization of gene therapies, including personalized therapies and the identification of biomarkers for success of these strategies.

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Alpha
This variant emerged in England in Sep 2020 and drove a winter surge in cases that sent the U.K. back into lockdown in January. Alpha was previously the dominant strain in the U.S., and has been reported in at least 173 countries, according to the WHO.
Beta
This one, which appeared in South Africa in Aug 2020, led to a resurgence in Covid cases that overwhelmed southern Africa. It’s been reported in at least 122 countries.
Gamma
This variant, first spotted in the Amazon city of Manaus in Dec 2020, has contributed to a surge in cases that strained Brazil’s health system and led to oxygen shortages. It’s been reported in at least 74 countries.
Delta
This fast-spreading variant,the Variants of concern “kappa” and variants of interest”Delta”.stoked a dramatic wave of Covid cases in India and has since been found in at 104 countries.

What is the Delta variant of Covid-19?
Multiple SARS-CoV-2 variants are circulating globally. One of these is the B.1.617 lineage, detected in India earlier this year. Early evidence suggests that its sub-lineage B.1.617.2, known as the Delta variant, is more transmissible than contemporary lineages.
The World Health Organization (WHO), which has given it the label Delta, has categorized it as a variant of concern (VOC). It has said it continues to observe “significantly increased transmissibility” and a “growing number of countries reporting outbreaks associated with this variant”.
WHO classifies a variant as a VOC when it is associated with an increase in transmissibility or detrimental change in Covid-19 epidemiology; increase in virulence; or decrease in the effectiveness of public health measures or available diagnostics, vaccines, therapeutics.
What makes the Delta variant a VOC?
Different variants are characterised by mutations — or alterations in the virus’s genetic material. An RNA virus, such as SARS-CoV-2, is made of about 30,000 base pairs of amino acids, placed like bricks next to each other.
An alteration in any of these base cause a mutation, effectively changing the shape and behaviour of the virus. The Delta variant contains multiple mutations in the spike protein. At least four mutations are important.
One of these is called L452R, first reported in Denmark in March last year. This mutation has been found more transmissible than wild-type strains and also been associated with reduced antibody efficacy and reduced neutralisation by vaccine sera.
The mutation P681R has been associated with chemical processes that may enhance transmissibility, PHE says.
The D614G mutation was first documented in the US early in the pandemic, having initially circulated in Europe. “There is evidence that variants with this mutation spread more quickly,” the Centers for Disease Prevention and Control (CDC) says.
Another mutation in Delta is T478K. This was present in around 65% of occurrences in variant B.1.1.222, first detected in Mexico last year and associated with higher infectivity.

What is the evidence so far on transmissibility?
Public Health England said Delta continues to demonstrate a substantially increased growth rate compared to Alpha across multiple analyses. In the week beginning May 17, PHE analysis of genome sequencing data in the UK found that 61% of cases are delta.
Delta cases are rising while Alpha cases are declining. Also, PHE said, secondary attack rates have remained higher for Delta than Alpha .
What is the evidence on severity?
PHE said early evidence from England and Scotland suggests there “may be an increased risk of hospitalisation compared to contemporaneous Alpha cases”. “A large number of cases are still within the follow-up period. In some areas, hospital admissions show early signs of increasing, but the national trend is not clear,” it said.
How effective are vaccines?
The PHE says there are analyses from England and Scotland supporting a reduction in vaccine effectiveness for Delta compared to Alpha. This is more pronounced after one dose. “Iterated analysis continues to show vaccine effectiveness against Delta is higher after 2 doses but that there is a reduction for Delta compared to Alpha,” it said.
On Friday, a paper in The Lancet said adults fully vaccinated with the Pfizer-BioNTech vaccine are likely to have more than five times lower levels of neutralising antibodies against the Delta variant than against other variants. “In the longer term, we note that both increased age and time since the second dose of BNT162b2 significantly correlate with decreased NAb activity against B.1.617.2 and B.1.351—both of which are also characteristic of the population in the UK at highest risk of severe Covid-19 ,” the study states.

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Gene therapy is coming to the fore as a way to treat the root cause of disease,rather than just the symptoms. Imagine a world where any genetic disorder can be cured by a single dose of a gene therapy treatment.

The best vector for the job
The goal of gene therapy is to correct disease-causing variations in the DNA of the patient’s affected tissue. Treating only the relevant (somatic) cells is an important difference to the controversial germline modifications, where any changes would be passed on to potential children. Delivering this targeted treatment machinery to the correct cells is a key challenge.
One solution to this challenge is to use the right kind of shuttle, or vector, to introduce the functional gene. The groups at CMRI, headed by Ian Alexander and Leszek Lisowski, primarily focus on the use of adeno-associated virus (AAV) as a vector to deliver gene therapies directly into specific cells within the patient. AAV is widely regarded as a safe and effective vector – it is utilized in the first FDA-approved in vivo gene therapy, Luxtruna, to treat an inherited form of blindness.
One benefit of using AAV is that it avoids any germline modifications, since upon transduction its genome is not integrated into the host genome. Therefore, even if the vector finds its way into germline cells, it is very unlikely to be inherited as in the vast majority of cases any DNA not integrated into the host genome is not passed on.

The safety of AAV can be enhanced even further through optimization. One key issue is the idea of tropism: ideally a treatment targets only a tissue(s) of interest, with only minor effects in other tissues. Therefore, viruses with a very specific tropism are required for potential treatments to be delivered only to a target tissue. This is an area in which the teams at CMRI have expertise, having developed methods to randomly recombine the outer shell, or capsid, of the virus that determines tropism, as well as clinically relevant models to perform selection of the best vectors for the job (Lisowski et al. 2014; Cabanes-Creus et al. 2018).
Despite these benefits, when using AAV as a vector the lack of integration into the host genome can also be an issue for long-lasting therapies. Since the therapeutic gene is outside of the host genome, the length of time it persists depends on a number of factors. However, in the vast majority of cases the DNA introduced by the vector is eventually lost, and any therapeutic benefit is lost with it.

CRISPR-Cas addresses persistence
Instead of aiming to deliver an additional functional copy of a mutated gene, one way to sidestep this limitation is to use a targeted genome editor. Again, guiding the editor to specific (non-germline) tissues, the introduced vector can make a lasting change such as correcting disease-causing change in the genetic code. An obvious candidate here is CRISPR-Cas9 which can be highly effective, specific and versatile.

By tweaking the tropism of the AAV vector, we can target only the cells of interest, avoiding editing in any tissues except those directly affected by the disease. The combination of a specific tropism conferred by AAV and genome editing using CRISPR-Cas9 is therefore an attractive prospect for safe and effective gene therapies.

A key caveat here, though, is getting the design right on the CRISPR side. The CRISPR-Cas9 system relies on a guide to target it to the correct place in the genome. If there are other places in the genome similar to the on-target site, outcomes for the patient could be disastrous. Therefore, the genome editing side of any therapy also should be carefully assessed before any clinical applications are even considered.
This is where the tools developed by the CSIRO Digital Genome Engineering team come in. Software tools such as TUSCAN (Wilson et al. 2018) and VARSCOT (Wilson et al. 2019) can help design the best possible guides for gene therapy applications, maximizing the therapeutic benefit while minimizing the risk of unwanted off-target effects. This digital approach to genome engineering lays the foundation for pre-clinical studies, ensuring that further assessments of safety and efficacy have the best chance of succeeding.
Is it safe?
So it is clear that before bringing any gene therapy to the clinic, especially one utilizing a genome editing technology, it first needs to be carefully assessed for safety. A key element is hence to investigate if and how parts of the vector genome are incorporated into a host. Although AAV vectors are not known to frequently integrate into the host genome, the introduction of double-stranded breaks by CRISPR-Cas9 might increase the risk of AAV integration. It is therefore important to challenge the assumption of ‘no integrations’ in the genome editing therapy context.
To assess this safety we are collaborating with CMRI, in another project to develop method for characterising viral and vector integrations into a host genome. The aim here is to interrogate any integrations to make sure they aren’t likely to cause any harm to future patients.
Ian Alexander’s team at CMRI have undertaken this kind of investigation before (Kamboj et al 2017; Hallwirth et al 2015), but in these investigations the viruses were known to integrate in a particular manner. In these studies they knew what to look for, but the current task of finding integrated AAV is challenging because these events might not occur in the same way, and are predicted to be rare. Finding them in the host genome might be like looking for a needle in a haystack. AAV also doesn’t appear to have particular patterns of integration in the human genome, so we need to take an unbiased approach to maximize the chance of finding any integration sites.
Although developed to investigate the integration of clinical vectors, this approach could also be used to uncover patterns of integrations of wild-type viruses and investigate their role in various pathologies.
New and improved gene therapies
Gene therapies utilizing CRISPR-Cas show great promise as effective and long-lasting treatments for genetic disorders. However, to realize this promise these prospective therapies must be rigorously tested for safety and efficacy, and there is still a way to go before this promise can be realised. By bringing together the expertise of the CMRI teams in developing gene therapies with our bioinformatics skills and in consultation with CSIRO’s Responsible Innovation Initiative, we’re contributing to these future treatments for genetic disorders.

References
Cabanes-Creus et al. Codon-Optimization of Wild-Type Adeno-Associated Virus Capsid Sequences Enhances DNA Family Shuffling while Conserving Functionality Mol Ther Methods Clin Dev 2018

Hallwirth et al. Coherence analysis discriminates between retroviral integration patterns in CD34(+) cells transduced under differing clinical trial conditions Mol Ther Methods Clin Dev 2015

Kamboj et al. Ub-ISAP: a streamlined UNIX pipeline for mining unique viral vector integration sites from next generation sequencing data BMC Bioinformatics 2017

Lisowski et al. Selection and evaluation of clinically relevant AAV variants in a xenograft liver model Nature 2014

Wilson et al. High Activity Target-Site Identification Using Phenotypic Independent CRISPR-Cas9 Core Functionality CRISPR J 2018

Wilson et al. VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9 BMC Biotech 2019

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When Ugur Sahin got to know in January 2020 about the emergent threat of SARS-CoV-2, he immediately started thinking about how BioNTech’s mRNA platform could be used to rapidly deliver a vaccine. His company and its partner, Pfizer, have since helped to rewrite the vaccine development textbooks. Their vaccine offers 95% efficacy, and secured emergency use authorization from the FDA in record time. The collaborators have delivered more than 700 million doses of their vaccine worldwide. Moderna, the other mRNA vaccine frontrunner, has also shipped hundreds of millions of doses of its vaccine. This remarkable feat has transformed interest in mRNA-based therapeutics — and the prospects for the companies who pioneered these. Before the pandemic, BioNTech’s lead candidate was a phase II cancer vaccine that was years away from the market. Now, it expects 2021 revenue of €12.4 billion for its COVID-19 vaccine. Researchers and investors, as a result, are lining up to work on mRNA platforms. For Sahin, this windfall provides a chance to rethink BioNTech’s development approach to its first priority — mRNA-based cancer vaccines. Despite high hopes for vaccines that teach the immune system to recognize cancer cells, these have so far failed to yield compelling activity in the clinic. New mRNA chemistries and delivery approaches, combined with recent insights into when to best apply these candidates, were already on track to overcome some of the pitfalls of the past, says Sahin. Bolstered by its COVID-19 achievements, BioNTech can now place bolder bets that could increase its odds of success, he adds.

Q. What have you learned from your success with a COVID-19 vaccine?

It was the first complete chain of demonstration that an mRNA vaccine, delivered in a well-tolerated dose, is able to induce neutralizing antibody responses and strong CD8+ and CD4+ T cell responses. And this translates into all kinds of effectiveness, including protection against disease, severe disease, hospitalization and infection, which is the highest hurdle. We’ve learned about the relative robustness of the vaccine-induced immune responses against variants. We’ve seen real-world evidence not only for the original parental strain, but also against the [Alpha] variant, which has a substantial number of spike protein mutations. We did not even see a significant reduction in efficacy. And we have now also seen this type of efficacy against the [Delta] variant, which is now the emerging variant. This is encouraging. The other good finding is that an mRNA vaccine is suitable as a booster. If subjects had pre-exposure to COVID-19, these subjects had strong antibody responses to our shot. There are emerging data showing the suitability of these mRNA vaccines after the viral vector vaccines as well, which is also encouraging. And we are also generating data for a third injection booster. One important insight is that repeated vaccination is possible and appears to be well tolerated.

Q. Prior to the pandemic, your first priority was cancer therapies. How much will you now focus on infectious disease vaccines?

We were always interested in infectious diseases, but they were not a priority. Now we have an approved product, and we have of course ideas about how to develop other infectious disease vaccines. For example, the first-generation approved product is a nucleoside-modified lipid nanoparticle-based vaccine. But we are working in parallel on self-amplifying vaccines [in which the mRNA construct carries instructions to make copies of its antigen-coding sequence] and trans-amplifying RNA vaccines [in which one mRNA construct carries the antigen-coding sequence and another carries the instructions to make copies of the first]. Each of these will have its own evolution. I believe that the trans-amplifying technology could have quite a few additional advantages, including reduced dosing that could enable multivalent efficacy.

Q. Do you expect this work on infectious disease vaccines to impact how much you can focus on cancer vaccines?

Well, we now have the funding to accelerate our cancer pipeline and make it even bolder. It’s not that we are shifting resources, but rather that we can now be much bolder than we were before. As an externally funded biotech company, we had to make our plans according to our funding. And that means of course compromises for the speed and the depth of our studies. With our new levels of resources, we can ask whether we would do our trials in the same way as initially planned, or whether we can do them in a manner that will provide us with better chances of success.

Q. How are those discussions playing out?

This is still an ongoing discussion here. And this is one of the surprises of our success, that we can reconsider our whole plan. But at the end of the day we will have more randomized trials, in more patients, run out of more clinical trial centres. And instead of a sequential de-risking approach, we can accelerate our development plans and ask five questions at once.

Q. Whereas the traditional approach to cancer drug development is to test new drugs in late-stage disease, cancer vaccines may need to be used in early-stage disease, when patients have functional immune systems. Does your new approach to de-risking include more evaluations of cancer vaccines in patients with early-stage disease?

Yes, absolutely. We believe the superpower of cancer vaccines is really in patients with early-stage disease. We recently started a clinical trial of the cancer vaccine BNT122 in patients with colorectal cancer who have undergone surgery, comparing chemotherapy alone to standard of care plus vaccine. This trial is using circulating tumour DNA as a biomarker. This is sort of a blueprint trial, in which we are enriching for early-stage patients with minimal residual disease. We will likely start a few of these.

Q. Some of your cancer vaccines teach the immune system to recognize ‘tumour-associated antigens’ that are shared between patients, while others focus on ‘neoantigens’ that are unique to each patient. Which of these is more likely to succeed?

I believe that both have their place. Shared, non-neoantigen vaccines have been considered to be weakly immunogenic. But we have shown that we can overcome this weak immunogenicity with a nanoparticulate mRNA vaccine that is targeted to dendritic cells. In a recent melanoma trial we showed that in patients who had progressed on checkpoint blockade, the addition of the tumour-associated antigen vaccine BNT111 resulted in a significant percentage of objective responses. Now, checkpoint blockade primarily activates T cell responses against neoantigens. Tumour-associated antigen vaccines seem to bring in an immune response against non-neoantigens as well, which are otherwise neglected and not empowered by checkpoint blockade. This creates new PD1+ T cells, which we can then re-sensitize with checkpoint blockade. That’s one hypothesis. This would be a completely new approach. These results motivated us to run a phase II trial evaluating checkpoint blockade plus vaccine versus checkpoint blockade alone versus vaccine alone in melanoma patients who have failed prior checkpoint blockade. We are also now running clinical trials where we combine CAR-T cell transfer with tumour-associated antigen vaccines to stimulate CAR-T cell activity. Then we have the neoantigen candidates, which we believe could be well suited for patients with early-stage disease, in the neoadjuvant or adjuvant settings. Colorectal cancer is an ideal example because there are no shared tumour antigens in this setting, if you ignore CEA. And because the mutational load in these tumours is relatively moderate to low, this type of tumour does not respond to checkpoint blockade at all. That means mRNA vaccines could be really well positioned here. We also have a phase II trial evaluating pembrolizumab plus the neoantigen vaccine BNT122 versus pembrolizumab alone in first-line melanoma that will read out in 2022.

Q. Beyond vaccines, there are hopes that mRNA approaches can be used to deliver therapeutic proteins too. How do you think about that opportunity?

We are doing this already. We have one clinical trial already running with BNT151, an mRNA that encodes IL-2. Already, there is a renaissance of interest in IL-2, and there are various companies using pegylated IL-2 to improve the pharmacokinetics of this cytokine. We have created a mutant IL-2 that does not activate regulatory T cells, and by delivering that with an mRNA approach we can further improve the pharmacokinetics. Rather than dosing IL-2 three times a day, we have the opportunity for 3-weekly dosing. We believe that this molecule could be complementary to checkpoint blockade, and we have seen in preclinical testing that vaccine responses are much stronger when combined with IL-2. We are also working on mRNA-encoded antibodies, and particularly bispecific antibodies. We think these too can overcome problems of suboptimal pharmacokinetics. We have shown that we can go for weekly dosing of bispecifics. And whereas bispecifics tend to form multimers and aggregates when they are stored in vials, we can circumvent that problem. We have invested a lot of effort in coming up with the right backbone for these candidates, and if the backbone works we can just exchange the sequences and could bring multiple candidates into the clinic. This could be a real door opener for bispecific antibodies. We have some evidence that they are even better tolerated than their original protein-based counterparts.

Q. The beauty of mRNA vaccines is that you turn a ‘bug’ into a feature: the immunogenicity of mRNA helps prime the immune system. How big a challenge is immunogenicity for mRNA-encoded proteins, cytokines and antibodies?

Immunogenicity is the greatest challenge. We have to see if we have invested enough in reducing immunogenicity. That is ongoing research. Another way to ask this question is: what are the lower-hanging fruits and what are the highest-hanging fruits of these applications? The highest-hanging fruits need high doses of a molecule that are injected repeatedly, for months or years, to ensure that you have a protein-replacement effect. We are not yet there, in my opinion. If we can accomplish that, then much more is doable. But you have to solve immunogenicity — both against the lipid nanoparticle itself as well as against the encoded protein. We are working on strategies to achieve this, but it’s too early to talk about these. But the technology will evolve via the development of mRNA-encoded products that are delivered just a few times. And that space will be significantly bigger than the infectious disease and the cancer vaccine space. Also, although we are talking about mRNA, it’s really about delivery. We have to acknowledge that we are reliant on the maturation of innovation from different technologies as well, including lipid nanoparticles and polymer-based delivery approaches.  

Q. There has been an influx of investment into mRNA technology as a result of the success of the COVID-19 vaccines. How has this impacted the field?

It’s fantastic that the field is now recognized. It feels like the early days of DNA cloning, which resulted in the founding of hundreds of companies. We don’t call these recombinant protein companies anymore, and I believe we should not call every company an mRNA company either. But of course there are technologies that will evolve, including circular mRNA and self-amplifying mRNA. We can’t forget that between mRNA and siRNA, there are microRNA approaches. The whole field will benefit.

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