Scientist and blogger Gianpaolo Rando is BioData Blogs Featured Scientist for the month of April. Read about trends in the development of reporter genes in his blog, reportergene.com
Tell us about your first encounter with science I was 12: I was a strong reader and a loyal fellow of the civic library. Books being the main source of my knowledge, I thought everything in the world had already been discovered. At the beginning of the school year, my new science teacher introduced a small aquarium to the classroom. He put in a mug full of water from a waterhole and asked the class to observe daily the little water box for two weeks. Life immediately developed and gradually faded when the food resources started to get out. I learnt the basics of ecosystems by direct observation. This was my first encounter with science and the first time I realized that knowledge can be obtained in real-time without need for a book. I asked myself: do you want to only be a reader or do you want to take part in the race toward knowledge? It took 10 years of 'boring' book-reading before I really started taking part on my own bench with tubes and pipettes.
What made you choose a scientific career? My curiosity. I'm curious, pathologically curious. I've always wondered how the world runs, and particularly the way living things work. What are the mechanisms and what are the dynamics? Choosing a scientific career was very natural.
What is your current area of research? I received a M.Sc. in Biotechnology and a Ph.D in Pharmacology at the University of Milan (I'm a former Pharmacologist). I've always been fascinated about how small molecules can influence our physiology and our mind. My interest particularly concerns so-called 'nuclear receptors', these are proteins that are able to directly sense the presence of small chemicals (i.e., hormones, metabolites, drugs) and to consequently modify gene expression by binding on selected DNA sequences on gene's promoter regions. Recently, I joined the Center of Integrative Genomics at the University of Lausanne where my current research focuses on the differences in nuclear receptor signaling between males and females.
Why did you decide to start a scientific blog? What reactions did you receive? It was an experiment. Four years ago I wondered which part of the world was most intellectually active in one particular field of molecular biology (the development of reporter genes). Of course pubmed can give some answers, but I wanted to know not only which labs were publishing most, but also about the public: Where the readers were? Where the discussion was? What was more popular: luciferase or GFP? So I registered reportergene.com and I started commenting on papers dealing with new technologies involving reporter genes. The idea was to resolve the IP address of the visitors and obtain a map of niches interested in the development of genetically-encoded approaches. The blogging platform was chosen because of its simplicity, but at that time I was not thinking of it as my blog. One year later I gradually realized that a scientific blog was an opportunity of immediate communication for a scientist. Young researchers can work three years or more before getting into a publication, and probably never being contacted about their work (if they aren't the corresponding author). Despite the vastness of the scientific community, PhD students and postdocs are still confined to their benches with limited social connectedness: you can speak every-day with your 10-20 lab-mates about your research or, once a year, try to catch some of the 100 conference attendees toward your poster. This is poor communication. In this sense, a blog is a short-circuit: you can debate your arguments every week with thousands of readers: writing a post takes the time of a coffee, but gives you immediate feedback on the topics you are interested in. You are still 'at the bench' working full-time on your project but once a week, your coffee break takes a world-wide dimension, and you immediately feel the world wide web of the scientific community.
Your last paper introduces a new method to classify drugs, what are the current problems in drug classification? There are several ways to classify a drug (i.e., chemical composition, etc), one criteria concerns drug action and it is based on a theoretical framework which dates at the middle of last century. In very simple words, if a drug activates a biological effect is an AGONIST. A second drug that competes with an agonist, preventing the effect, is an ANTAGONIST. Today, because of our better understanding of the complexity of intracellular signalling, this definition becomes of difficult application. Most of the drugs act by binding to a cellular receptor: upon binding, drugs 'activate' the receptor, initiating the signaling cascade responsible for the biological outcome; we know that a single drug can induce different signaling cascades, and this ability may change significantly with respect to the tissue where the drug is acting and the time after drug administration. In short, a candidate drug can be agonist in the first week of therapy and suddenly may became antagonist (with obviously deleterious side effects!). Likely, such a candidate drug would be discarded in the drug development pipeline: in fact, it is common for unforeseen toxic profiles to be discovered late in the pipeline, already at the level of clinical studies in humans. This is unwanted: a failure of a clinical study is a great loss for a pharma-company. In conclusion, pure semantical problems about classifying drug action may contribute to stagnation in the biomedical economy other than being a potential hazard for human volunteers.
What is the current model of understanding drug effects over time? Current models belong to a discipline called Pharmacokinetic (PK) which studies the drug distribution in the body over time. With PK we can predict the presence of the drug in a particular organ, however, we don't get any information about the pharmacological effect. Drug action is studied by a second discipline called Pharmacodynamic (PD). My PhD dissertation deals with the possibility to introduce new dimensions (space and time) in PD studies by monitoring drug action on living 'luciferase reporter-mice' developed by my PhD mentor: Adriana Maggi. These animals start glowing when and where a drug is active: for the first time in the history we can study drug effects in a safe living mammals without the need to kill them. We are developing the technology to follow drug activity in different anatomical areas several times in a day for long period of times (months) without actually killing any animals. This new knowledge would have the power to disclose potential side-effects before getting into clinical studies, with clear social, ethical and economical benefits for men (and mice also!).
Why a SERM was chosen as a model? Selective Estrogen Receptor Modulators (SERMs), were conceived for post-menopausal hormonal replacement therapy to avoid estrogen unwanted effects on breasts while retaining their beneficial effects in other organs. As their name says, SERMs activity on the cognate receptor is 'selective' – this means that a SERM that blocks estrogen's action in breast cells may activate estrogen's action in other cells, such as bone. To date however, none of the SERMs developed appears to be provided of the ideal balance of ER agonist and antagonist activity. Because SERMs do not fall into distinct categories of agonists and antagonists, they represent a great 'proof of concept' to further elaborate on drug classification. Furthermore, menopause is one of those permanent conditions in which a drug is supposed to be administered chronically: the time dimension can not be further neglected.
References: Rando, G., Horner, D., Biserni, A., Ramachandran, B., Caruso, D., Ciana, P., Komm, B., & Maggi, A. (2010). An Innovative Method to Classify SERMs Based on the Dynamics of Estrogen Receptor Transcriptional Activity in Living Animals Molecular Endocrinology, 24 (4), 735-744 DOI: 10.1210/me.2009-0514