̽��ֱ�� of Cambridge - James Peters

Scientists create ‘genetic atlas’ of proteins in human blood

cjb250 — Wed, 06 Jun 2018 17:00:44 +0000

̽��ֱ��study, published today in the journal Nature, characterised the genetic underpinnings of the human plasma ‘proteome’, identifying nearly 2,000 genetic associations with almost 1,500 proteins. Previously, there was only a small fraction of this knowledge, mainly because researchers could measure only a few blood proteins simultaneously in a robust manner.

̽��ֱ��researchers used a new technology (“SOMAscan”) developed by a company, SomaLogic, to measure 3,600 proteins in the blood of 3,300 people. They then analysed the DNA of these individuals to see which regions of their genomes were associated with protein levels, yielding a four-fold increase on previous knowledge.

“Compared to genes, proteins have been relatively understudied in human blood, even though they are the ‘effectors’ of human biology, are disrupted in many diseases, and are the targets of most medicines,” says Dr Adam Butterworth from the Department of Public Health and Primary Care at the ̽��ֱ�� of Cambridge, a senior author of the study. “Novel technologies are now allowing us to start addressing this gap in our knowledge.”

One of the uses for this genetic map is to identify particular biological pathways that cause disease, exemplified in the paper by pinpointing specific pathways that lead to Crohn’s disease and eczema.

“Thanks to the genomics revolution over the past decade, we’ve been good at finding statistical associations between the genome and disease, but the difficulty has been then identifying the disease-causing genes and pathways,” says Dr James Peters, one of the study’s principal authors. “Now, by combining our database with what we know about associations between genetic variants and disease, we are able to say a lot more about the biology of disease.”

In some cases, the researchers identified multiple genetic variants influencing levels of a protein. By combining these variants into a ‘score’ for that protein, they were able to identify new associations between proteins and disease. For example, MMP12, a protein previously associated with lung disease was found to be also related to heart disease – however, whereas higher levels of MMP12 are associated with lower risk of lung disease, the opposite is true in heart disease and stroke; this could be important as drugs developed to target this protein for treating lung disease patients could inadvertently increase the risk of heart disease.

MSD scientists were instrumental in highlighting how the proteomic genetic data could be used for drug discovery. For example, in addition to highlighting potential side-effects, findings of the study can further aid drug development through novel insights on protein targets of new and existing drugs. By linking drugs, proteins, genetic variation and diseases, the team has suggested existing drugs that could potentially also be used to treat a different disease, and increased confidence that certain drugs currently in development might be successful in clinical trials.

̽��ֱ��researchers are making all of their results openly available for the global community to use.

“Our database is really just a starting point,” says first author Benjamin Sun, also from the Department of Public Health and Primary Care. “We’ve given some examples in this study of how it might be used, but now it’s over to the research community to begin using it and finding new applications.”

Caroline Fox MD, Vice President and Head of Genetics and Pharmacogenomics at MSD, adds: “We are so pleased to participate in this collaboration, as it is a great example of how a public private partnership can be leveraged for research use in the broader scientific community.”

̽��ֱ��research was funded by MSD*, National Institute for Health Research, NHS Blood and Transplant, British Heart Foundation, Medical Research Council, UK Research and Innovation, and SomaLogic.

Professor Metin Avkiran, Associate Medical Director at the British Heart Foundation, said: “Although our DNA provides our individual blueprint, it is the variations in the structure, function and amount of the proteins encoded by our genes which determine our susceptibility to disease and our response to medicines. This study provides exciting new insight into how proteins in the blood are controlled by our genetic make-up and opens up opportunities for developing new treatments for heart and circulatory disease.”

* MSD (trademark of Merck & Co., Inc., Kenilworth, NJ USA)

Reference
Sun, BB et al. Genomic atlas of the human plasma proteome. Nature; 7 June 2018; DOI: 10.1038/s41586-018-0175-2

An international team of researchers led by scientists at the ̽��ֱ�� of Cambridge and MSD has created the first detailed genetic map of human proteins, the key building blocks of biology. These discoveries promise to enhance our understanding of a wide range of diseases and aid development of new drugs.

Compared to genes, proteins have been relatively understudied in human blood, even though they are the ‘effectors’ of human biology, are disrupted in many diseases, and are the targets of most medicines

Adam Butterworth

qimono

Red blood cells

Researcher Profile: Benjamin Sun

“My work involves analysing big 'omic' data,” says Benjamin Sun, a clinical medical student on the MB-PhD programme at Cambridge. By this, he means data from genomic and proteomic studies, for example – terabytes of ‘big data’ that require the use of supercomputer clusters to analyse.

“My aim is to understand how variation in the human genome affects protein levels in blood, which I hope will allow us to better understand processes behind diseases and help inform drug targeting.”

Benjamin did pre-clinical training at Cambridge before intercalating – taking time out of his medical training to study a PhD, funded by an MRC-Sackler Scholarship, at the Department of Public Health and Primary Care.

“Having completed my PhD, I am currently spending the final two years of my programme at the Clinical School to complete my medical degree. My aim is to become an academic clinician like many of the inspiring figures here at the Cambridge. Balancing clinical work with research can sometimes be tough but definitely highly rewarding.”

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Drugs: how to pick a winner in clinical trials

lw355 — Mon, 26 Jun 2017 11:00:12 +0000

“Did not meet primary endpoint.”

Prosaic words, but they can mean a billion dollar failure has just happened.

̽��ֱ��average cost of taking a scientific discovery all the way through to a drug on a shelf is enormous – last year it was estimated at $2.6 billion by the Tufts Center for the Study of Drug Development.

One reason the figure is so high is because it also includes the cost of failure. Recent years have seen some very high-profile failures of drug candidates that either did not meet the ‘primary endpoint’ (they didn’t work) or had their trials halted owing to serious side effects.

“It’s only natural that some drugs will fail in clinical trials – the process exists to ensure that treatments are safe and effective for patients,” says Professor Ian Wilkinson, Director of the Cambridge Clinical Trials Unit (CCTU) on the Cambridge Biomedical Campus. “But what’s unexpected is the high number of drugs that fail in phase III. You’d think that by this stage the molecule would be a sufficiently good candidate to make it through.”

He explains that failures in phases I and II – when the drug is tested for safety and dosage in healthy volunteers and patients – are inevitable. However, a great many molecules don’t make it through phase III, the stage at which the drug’s effectiveness is tested in large numbers of patients before regulatory approval is given. In fact only 10–20% of drugs that enter phase I are ultimately licensed.

“ ̽��ֱ��problem with failing at phase III is it’s very expensive – a single drug trial can cost around $500m.”

He continues: “There’s a human impact for the thousands of patients who enrolled on the trial. For patients with cancer, it’s sometimes their last available treatment option,” says Wilkinson. “It’s also really unhelpful economically. Pharma companies have less money to put back into R&D, and it becomes even harder to fund drug development.”

This is why Wilkinson, along with a team of clinicians, scientists and pharmaceutical collaborators, together with statisticians at the Medical Research Council Biostatistics Unit, has been taking a hard look at the early phases of clinical trials. Their aim is to ask what can be done to get an early indication that a potential drug will make it to market.

“Traditionally, clinical trials have been organised to test safety first and efficacy last,” he explains. “It’s a cautious step-by-step approach adopted to ensure that pharma companies can satisfy regulators that the drug is safe.

“For many drugs this has worked well. But we are in a landscape where drug targets are more challenging – think for instance of conditions like psychiatric disorders and dementia. Leaving questions of whether a drug is effective to the final stages is now too risky and expensive.”

On any one day, the CCTU (one of the UK units accredited by the National Institute for Health Research) might be coordinating up to 20 trials in various phases for potential treatments for cancer, stroke, infections, dementia, heart attack, and so on.

Many of the trials are now designed with what Wilkinson calls “added value” built in at very early stages to give indications of whether the drug might work. This could include a biomarker that shows a drug for cirrhosis is reaching the liver, or a drug for heart disease is lowering cholesterol. “These are read-outs. They don’t show the drug works for the disease, but if the results are negative then there’s no point in progressing to later stages.”

̽��ֱ��trials are also run ‘adaptively’. “We look at data for each person as it comes in… once we have enough information to guide us, we make a decision that might change the trial. It’s a quite different approach to the traditional rigidity of trials. It maximises the value of information a trial can yield.”

In recent years, pharmaceutical companies like GSK and AstraZeneca (AZ) have championed the need for rigorous trial design to weed out likely failures earlier in the process.

GSK has its only trials unit in the UK in the same building as the CCTU. There, GSK researchers work alongside Cambridge clinicians and scientists on first-in-man studies. A more targeted approach to testing medicines in patients is a key component of a Strategic Partnership between GSK, the ̽��ֱ�� of Cambridge and Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust (CUH), which has the long-term ambition of jointly delivering new medicines to patients in the next five to ten years.

A few years ago, AZ analysed its drug pipeline before embarking on a major revision of its R&D strategy to increase the chance of successful transition to phase III and beyond. One area AZ identified as being crucial to success is to identify a causal relationship between target and disease. This might seem obvious but so-called mistaken causation has led to late failures right across the drugs industry. ̽��ֱ��usual cause is confounding – where a factor that does not itself cause a disease is associated with factors that do increase disease risk.

Professor John Danesh and colleagues at the Department of Public Health and Primary Care have pioneered a new way of finding evidence for causality before a patient is ever involved. Called ‘Mendelian randomisation’, it’s akin to a trial carried out by nature itself.

“Misinterpreting correlation as causation is a big problem,” explains Dr James Peters, who works with Danesh. “An increase in a protein biomarker in patients with atherosclerosis might suggest it’s important in the disease, but it’s not a valid drug target unless it plays a causal role. ̽��ֱ��conventional way to test this is to block the protein with a drug in a clinical trial, which is expensive, time-consuming and not always ethical.

“In phase III trials, the randomisation of participants helps to average out all differences apart from whether they are receiving the drug. Instead, we take advantage of the natural randomisation of genetic variants that occurs during reproduction.”

Some genetic variants can increase or decrease certain proteins that have been linked to a disease. If these variants can be identified – by computationally analysing enormous genetic datasets – then researchers can compare groups of people to see whether having the variant also increases the risk of a disease.

̽��ֱ��team has used this method to look retrospectively at why two phase III trials for a potential cardiovascular drug failed. “ ̽��ֱ��genetic evidence showed that the drug target was not valid,” says Peters. “We would have advised against taking this drug to a clinical trial.”

But it’s not just about predicting failures, Danesh’s team is picking winners. Evidence for the role of an inflammatory protein in atherosclerosis has now resulted in a clinical trial to see if an arthritis drug can be repurposed for atherosclerosis.

̽��ֱ��researchers are helping industrial collaborators to prioritise potential drug targets and predict side effects. They also hope to expand their capabilities to test large numbers of variants for different potential targets in an automated fashion – a high-throughput approach to therapeutic target prioritisation.

Meanwhile, Wilkinson is planning ahead to avoid a different type of limitation: expertise. “There is a lack of individuals trained to design and deliver innovative clinical trials, and this is now impacting on drug development,” he explains.

Last year, an Experimental Medicine Training Initiative was launched to train medics how to run innovative clinical trials. Wilkinson is its Director and it’s supported by the ̽��ֱ�� in partnership with CUH, Cambridge Biomedical Research Centre, and AZ/MedImmune and GSK.

“We all believe that the failure rate for drug candidates making it through phase III is unacceptably high,” he says. “Less than one in a thousand molecules discovered in the lab make it through to being a drug. We want to be sure that we can answer the billion dollar question of which are most likely to be winners.”

Read more about research on future therapeutics in Research Horizons magazine.

When a drug fails late on in clinical trials it’s a major setback for launching new medicines. It can cost millions, even billions, of research and development funds. Now, an ‘adaptive’ approach to clinical trials and a genetic tool for predicting success are increasing the odds of picking a winner.

We all believe that the failure rate for drug candidates making it through phase III is unacceptably high. We want to be sure that we can answer the billion dollar question of which are most likely to be winners.

Ian Wilkinson

Gatis Gribusts

Medication

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