Quantifying Antibiotic Resistance Evolution in Clinically-Relevant Microbes

Lead Research Organisation: University of Exeter
Department Name: Biosciences


Drug resistance is often observed when we treat infected patients with drugs that were discovered,
or designed, usually at great cost, with the express purpose of curing people of their infectious disease.
This happens, for example, to HIV patients, malaria sufferers or when a pathogenic microbe, like E. coli, finds its way into someone's bloodstream. Cancers can soon become resistant to the chemotherapeutic agents we throw
at them too, & all because of evolution.

The evolutionary march towards drug resistance can take time. It can be years, or
decades, after the introduction of a new drug before we see confirmation of clinical resistance to it and
a ten-year timescale is thought typical of many antibiotics. Unfortunately, this stops pharmaceutical companies from
seeking new antibiotic molecules. After all, why should they spend 10 years, at great cost, seeking to cure
a disease with a pill that is profitable in the marketplace for only 10 more years?

Intriguingly, drug resistance in tumours is seen in patients on a much shorter timescale,
sometimes within months of the start of chemotherapy, depending on the drug used, the tumour
type, and on the individual patient. So why should we not observe a similar phenomenon for antibiotics?
In fact, we do, & we are now seeing the emergence of datasets showing that bacterial pathogens
can evolve resistance within individual patients because of changes to the DNA of that bacterium
in a matter of mere weeks, even days; & it can be lethal.

This proposal cites a 2015 study (Blair et al, PNAS) whereby resistance to antibiotic treatment in a
blood-borne Salmonella infection was traced, week-by-week, over a 20-week period, whereupon the patient died.
That whole-genome sequencing study, using a range of computer and physical modelling techniques
designed to track evolution in real time, showed very precisely how the resistance profile of the infection quickly
changed by altering expression levels and structures of a variety of proteins within the Salmonella population.

Within a week the population had doubled the amount of efflux protein it was making, moreover, it was now making even better efflux proteins than the original, infecting Salmonella. The efflux proteins are used to pump the antibiotics from inside Salmonella cells to prevent the antibiotic from hitting its target, so they stop working, but this was just one of a variety of mechanisms identified that were shown to correlate with the changes in drug resistance that took place during treatment.

It is important to mention 'plasmids', loops of DNA that are disseminated across the planet by different
microbial species that provide resistance to a range of antibiotics, given these, it seems our future ability to deal with microbial infection sits in a terribly parlous state if something is not done to mitigate such rapid evolution. But what can be done?

Importantly, the 2015 study hints at possibilities. It shows that bacteria become susceptible to some
antibiotics as they increase resistance to others; in other words there are cross- or collateral-sensitivities that emerge
during treatment. So, sometimes, one could use one, and then another antibiotic. This is not outlandish, it is an
idea that has been trialled in the clinic for Helicobacter pylori infections, but little else, so we now need to find
novel cross sensitivities. We also need new ways of combining antibiotics into novel cocktails, & some of those are
proposed here too.

I claim that by bringing to bear modern tools of mathematical modelling and data analysis on microbes that
are subjected to antibiotics in the laboratory, by observing how they respond, we can find weak spots
in their defences that will help clinicians design new therapies & give pharma companies new
methodologies to use within their analysis pipelines. Indeed, this is happening now & I am seeking funding to continue the efforts of my group in this task.

Planned Impact

The purpose of this proposal is to broaden the impact of a research programme I began 7 years ago whose goal it was to formalise concepts about antibiotic treatment, going from theory, to the lab & now to the clinic.

Then, I reasoned that mathematical models could build on our increasing dexterity at manipulating microbial genetics & the increasing ease with which we gather evolutionary data to make wholly new contributions to how evolutionary biologists saw the path towards antibiotic resistance. I thought we might generate new
ideas for treatment along the way because the process of mathematical modelling provides very different ways of thinking to those medical & clinical microbiologists are used to.

While those differences provided opportunities, they also presented difficulties both in communication and in finding clinical relevance of the theory, but the first stage of the fellowship was spent overcoming these difficulties and in finding common ground.

That approach was highly worthwhile and the subsequent outcomes were important. The impact of that process on our understanding of just how rapid drug-resistance evolution is has been a feature of the papers we publish in world-leading journals. For example, I was able to show that synergistic antibiotic interaction patterns could morph in antagonistic patterns because of drug-resistance evolution; this is new.

The impact I first sought was realistically narrow, targeting a better understanding
of the "evolutionary pharmacology" of antibiotics. But I am now in a position to build on this & I am broadening the scope of those prior studies to actively, & realistically, seek impact in the clinic.

Although there is natural chasm between mathematical modelling studies and clinical treatments, I have identified two routes to clinical impact to push over the next two years.

First, I will publish analyses showing that mathematical models are consistent with the idea that antibiotic "mixing" and "cycling" are not likely to show appreciable differences in clinical trial datasets on the degree of antibiotic resistance they each select for. Core to the field, this is a controversial topic so this will be an important
contribution written to be accessible to a wide community of non-mathematical researchers.

Second, Project 1 describes a study that will show how different patients, with their different genomes, metabolise antibiotics differently. This means those patients are likely to need different treatment regimens, but those treatments can only be explored once we have some understanding of how the human genome
impinges on the antibiotic dosage in the body. This proposal will commence that study, initially using an in vitro system we recently designed for this purpose.

As my research programme has developed, it has become increasingly clear that the treatment of cancers, bacterial infection, plant fungal species, malaria, even HIV, share common features despite their many differences. One of my goals is to share ideas across communities where drug resistance is a core problem in order to form a consensus on "best practise".

It is through this common ground that I began to talk with Astazeneca over a year ago as we share a large number of questions that we both tackle from a common mathematical modelling perspective. We are now engaged in seeking solutions in each other's domain where our main overlap now is optimal combination design. We independently hit on the idea that one protein could be targeted with two drugs to keep resistance at bay & we are now testing this together, in both cancers and microbes.

Finally, if I am able to develop the culture device described in project 3 then this has the potential to benefit a wide variety of microbiological labs working in universities that are interested in studying drug resistance in a spatially-extended context. There are many of these but that requisite device is currently absent from the market.


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Description We've re-written a mathematical theory that has lead to the re-formulation of extensive clinical trials concerning antibiotic usage - to summarise a long story, the trials are extensive and have gone on for around 30 years now, but they were all based on incorrect mathematics. Not just incorrect assumptions but quite literally incorrect mathematical analyses, which has (in our view) lead to the failure of the trials from the moment of their
inception and so we've corrected that body of theory. We have written theory papers and expository papers so that clinicians can understand the outcomes and to try and explain how such a series of, what turned out to be, incredibly important errors were made. The most recent clinical trials published (eg the Saturn trial) agree qualitatively with our findings - we are the only research group globally that can claim this - and we showed why very different antibiotic management practises in hospitals should result in similar incidences of drug resistance, contradicting the work of other research groups.

The grant has lead to the development of other work that is still in process but of the greatest clinical relevance is a study of the evolutionary genomic properties of a hospital antibiotic resistance test that creates a dataset known as an "antibiogram" which is based on a type of lab-based experimental assay. We have shown that the textbook analysis of this experiment has some interesting flaws in terms of the interpretation of its data that we've corrected and some of those corrections are relevant to how quickly the genomes of bacteria can change, particularly when bacteria evolve in drug gradients that these hospital-based tests necessarily produce by design. A key point of this in terms of the biology is how we have shown that so-called "copy number variation" (CNV) can be incredibly important for antibiotic resistance, something that tends to get ignored even in the genomics literature on antibiotic resistance. The CNV can be so substantial that the genome of a bacterium can (almost) double in size overnight in order for it to try and overcome the presence of an antibiotic.

While the theory has impact within the clinical community, this work has led us to be part of two pre-clinical research projects regarding the evolution of resistance in patients. One in Bristol and one in Sydney, Australia where the latter incorporates a novel application of phage to the treatment of sepsis. We are seeing evidence of the genomic changes in patients that we predicted from our lab-based studies, some for patients suffering from bloodstream (and other) infections that can last as long as 2-3 years.
Exploitation Route The re-design of clinical trials to better use antibiotics is possible from our work, although highly technical from a clinical perspective because of the depth of the underlying theory. This theory also provides a framework for understanding clinical datasets in some failed antibiotic stewardship trials.

Our biggest hope for future impact rests in the high-fidelity device we are making that can measure antibiotic resistance properties of bacteria and that work is progressing well, helped by ERC Proof of Concept and EPSRC-IAA awards that helped us work with a robotics startup to advance the project to the point where we now have working prototypes with very high data quality. The IAA was invaluable for allowing this to happen - ideally we will be able to use this in research labs, clinical labs and even schools. The latter can be done as part of biotechnology and biology lessons because our work and the device spans the fields of maths, software development, biotechnology and pharmacology. The University of Pittsburgh is likely to be our first client for this device, but many other universities are likely to become partners too, from a drug discovery centre in Birmingham University to a phage research centre in San Diego.
Sectors Creative Economy,Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology

URL http://people.exeter.ac.uk/reb217/rebHomePage/publications.html
Description Some of our findings have become part of a council document in Stoke on Trent that seeks to to change the nature of STEM education in that city (to improve it from the lowly position it currently occupies nationally). This centres on our use of low-cost robotics to produce laboratory kit for science education that can be used in classrooms to teach biotechnology and coding/statistics/mathematics. This particular impact stems from my position on a part of the "SASCAL" committee in Stoke which brings together teachers, school heads, council members and universities to create a new STEM teaching culture by leveraging university expertise and thinking by bringing it into schools. This committee recently created a new science festival that we have run, starting July 3-4 2017. We have now initiated this as an annual event and have used it as a basis to trial biotechnology kit in schools that is produced by my lab, and funded by this award. The festival has been registered with the UK Science Festival Networks run by the British Science Association and we are now seeking co-funding with schools to further this work. As this impact has continued to gain momentum, I'm now part of several different Opportunity Area educational grants in Stoke and we are targeting the use of our low-cost technology in schools as soon as possible. When we discuss this project with teachers, we know it by as the 'Lab in a Bag' project that is now very close to fruition. Indeed, we have applied for a patent for a low-cost spectrophotometry device developed as part of this award and are in the process of dealing with the responses to that application. This device is important for our Lab in a Bag project because it allows us to undertake microbial screening work, for properties like antibiotic resistance, at a fraction of the cost of commercial devices and it is this property that allows us to deploy it in schools and we have every intention of developing this into a spinout company, not least because the data quality we can produce is of a professional research grade. This work is currently funded by means other than UKRI (namely ERC) although an IAA award from EPSRC helped kick start the original concept.
First Year Of Impact 2015
Sector Digital/Communication/Information Technologies (including Software),Education,Healthcare,Manufacturing, including Industrial Biotechology
Impact Types Cultural,Societal,Economic

Description Dr Jon Iredell 
Organisation Westmead Hospital
Country Australia 
Sector Hospitals 
PI Contribution Discussions around the clinical testing of some of our lab-based ideas are underway, grant-writing around the potential for clinical trials and the design of novel biomedical devices.
Collaborator Contribution Giving a clinical perspective on the antibiotics work we do in the lab.
Impact The one output is one research paper in this portfolio, thus far, and a good deal of ongoing activity. Outside impact isn't clear yet but there are clear plans for this collaboration between a medical infection group and a mathematical-evolutionary research group.
Start Year 2013
Description There are dozens of school visits that have arisen from this work, we've also started a science festival in Stoke-on-Trent and are part-funding it from the impact plan award provided in this grant. 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Schools
Results and Impact Over a hundred school children and more than ten school heads, a local council and many teachers in the Staffordshire area are part of this work - it's such a large project it would take a lot of text to summarise it. We are trying to change the STEM/science culture in a city that really needs it (Stoke on Trent) and plans are slowly growing, ideally to the point where we could replicate the Exeter Maths Sixth Form within Stoke. The local council and local schools are interested in this project but funding (e.g. DfE) is the next key step to making that happen.
Year(s) Of Engagement Activity 2016