a Bioinformatician in the Big Pharma

The last 18 months have been quite a radical career change for me. This is because I made the infamous move: leaving the Academia and starting working in the Industry.

My career from Bologna to Barcelona, London, and the British Countryside. Yes, England is really more rainy than Barcelona.
My career from Bologna to Barcelona, and from London to the British Countryside.

To be honest I am quite happy of the change. I’ve learned many things, discovered another way to do science, and possibly made some contributions. Moving from the Academia to Industry sometimes has a bad reputation, but these months taught me that to develop a drug, there are many resources to be involved: not only a smart idea in the lab, but also lot of validation, regulation, planning, marketing, budgeting, understanding the impact on the patients, and much more.

Where am I working exactly?

I am in the pre-clinical department of a big pharma company, GSK. More specifically my department is called Target Sciences, and the main scope is to identify and validate new targets (in layman terms: genes or biological entities) to treat indications (in layman terms: diseases or phenotypes).

The R&D department of GSK is structured in several Discovery Performance Units (DPUs), which are small independent units working on a specific therapy area. For example there it could be a DPU focused on Oncology, or another on Asthma and respiratory diseases. These DPUs are like small start-ups within the company, and they each carry out a few drug target through the drug discovery process.

Drug discovery process – I am in the first phase. Source: https://www.slidegeeks.com/shapes/product/business-steps-powerpoint-templates-marketing-drug-discovery-process-ppt-slides

My department helps all these DPUs identifying and evaluating drug targets, providing several computational biology expertise, together with genetics, stats, and experimental validation. It’s like a center of excellence which interacts with all the rest of R&D.

Identifying the correct target is important because it is the first decision in the drug development process, and an error in this step can be quite expensive. Imagine what happens when a clinical trial fails in phase III because the original drug was targeting the wrong gene: it is quite a big waste of resources, not only for the company but also and more importantly for the patients.

What is target identification, and what is my role

In layman terms, identifying a drug target involves answering the following question: if I want to treat disease X, which would be the best genes to target?

From a computational point of view, there are several ways to answer such question. You may simply go to the literature (e.g. pubmed) and search for relevant articles. Other approaches involve looking at information from several sources, like gene expression, protein interactions, involvement in pathways, and much more. It is usually a matter of data integration, or data science.

If you want to get a more general idea of the types of sources used for target identification, you can have a look at the Open Targets Platform; this is a pre-competive effort to curate and integrate data sources, supported by the EBI, GSK and other pharmas.

My role, in particular, is more focused on data integration and management than pure analysis. It is about making the best use of the datasets we have access to, and understanding what is the value of acquiring a new dataset. It is also about improving communication about data usage, and discovering new technologies and methods to make use of the data.

What is good about working in a pharma, compared to academia?

Let’s say three things:

  • Team Working. This is the answer that hurts the most, specially me.
    If you look at the previous posts in this blog, you can see how much I care about doing science in a agile way, planning properly and sharing information. The problem is that in the academia, the pressure of having to publish first author papers ruins it all.
    In the academic world there is a lot of collaboration, specially online, and team meetings and journal clubs; but at the end of the day, your long term prospects are all dependent on your own reputation in the scientific world. This is fair enough, but difficult to reconcile with real team working.
  • Lots to learn: everybody is usually involved in more diverse projects, and interact with more people from different background. Thus, you tend to specialize less in a specific area, and learn a bit of everything. To be honest, I prefer this approach as it keeps the attention higher. I am glad that I did a PhD, during which I spent several years specializing on a single area, human genetics; however, now that I got older I like learning more about different fields.
  • Possibility to grow. You are generally more pampered and cared than in the Academia. You are actively encouraged to follow courses and learn new technologies; and my line manager complains if I am still in the office after 6 pm. (to be honest my PhD supervisor also did). There are opportunities to do secondmends in other parts of the company, and learn about clinical trials, finance, or anything related. Every year you define a list of objectives with your line manager, and you are valued depending on how you reach them, in a fair process, and you are valued for your efforts and accomplishments.

What is Bad?

  • Politics. Unfortunately politics is everywhere, specially in a big international company. Luckily I am still unimportant enough, that this doesn’t affect me much.
  • Simplification. Interacting with people with different background means that you need to simplify and learn to explain complex biological concepts in a way that is easy to understand. This is not easy and sometimes lead to funny effects, e.g. when you start hearing buzz-words and simplifications. On the bright side, at least I am improving my communication skills.

What’s next?

For personal reasons I haven’t written much in this blog lately, and I may not be able to write much in the near future. However, hopefully I’ll be able to write more about this new adventure, and describe how science is done from the industry side.

my first DataDive event

This has been a lovely and sunny weekend in London, but I didn’t see any of it because I spent it all crunching dataframes and calculating numbers at my first Data Dive.


Data Dives are events organized by an international organization called DataKind, in which a bunch of data scientists volunteer to dedicate their time to solve data analysis for non-profit companies. For example I have been analysing data for My Help at Home, a company that helps elderly people finding local carers, trying to understand which factors influence the demand and costs of private carers.

DataKindUk has a strict no-sharing policy regarding the results of the Data Dive, in order to protect the data made available by the charities. However in the case of My Help at Home we used only publicly available data, so I guess I can show some of the results, based on the number of Homes, Agencies and Hospitals in UK:

Here are a few thoughts about the experience:

  • I’ve decided that I will start introducing myself as a data scientist rather than a bioinformatician. Most people from outside the academia do not really understand what a bioinformatician is, and it is easier to explain them that you are a data analyst or scientist working on genetic and biological data. In the end the definition is correct – bioinformaticians truthfully are a specialized type of data scientists.
  • This has been an opportunity to get in contact with the “real world” of data science outside the academia. Most of the people I met work for the private sectors, like financing, consulting, gambling, and journalism. I only met a couple of people from the academia, and they were both complaining about the lack of organization and planning at the university.
  • Thanks to dplyr and related libraries, R has become a really powerful tool for merging and assembling datasets. It helped me a lot during the phase of data cleaning and assembly, and I think that for these tasks it is much better than python or bash. I would recommend to anyone starting learning R to skip all the basic syntax and start directly with dplyr (e.g. see the tutorial I wrote for the PEB workshop).
  • The majority of people used python, in particular the ipython notebooks, for most of the tasks. Currently I am a R and dplyr person, but for machine learning tasks I am starting to think that python and scikit-learn can actually be more powerful.
  • People working in consulting, who for their work need to able to easily create nice and interactive graphs, used visual solutions such as tableau rather than munching with R or other programming tools. For example, the interactive graph above was created in a couple of minutes with noveau.

Recruiting mentors for MindTorch

I have a small announcement to make. In the last months I became involved in MindTorch, a London based start-up that aims at matching students and young researchers with potential career mentors. It is important for young people to have a mentor or a person of reference to advise them, telling them how to invest in their future and which mistakes to avoid. MindTorch aims at helping people finding exactly that, by providing a community where everyone can find a mentor.

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We are currently in the phase of recruiting mentors – that is, professional or researchers with good experience and who would be willing to dedicate some time to help and counsel younger people. Every mentor is supposed to dedicate one hour every month to their mentee, for three to six months and starting from next October/November, plus some initial time to communicate with us. So, if you would like to volunteer as a mentor for MindTorch, contact me or register as a mentor on the website.

In summary:

Do I have enough experience to mentor someone? If you have a degree and job experience you can certainly be a good mentor. We will train you and support any doubts you may have.

How much time and effort will it take? You will have to dedicate one hour every month to counsel a younger student, for three to six months.

What do I get from being a mentor? For the moment we are not planning on giving any monetary retribution to mentors, but you will get to learn a lot from the experience and have the opportunity to grow your network of contacts. We will also help you finding mentors and new contacts for your own career.

How to join: register as a mentor on the website.