Ajith Madhavan
Assistant Professor, School of Biotechnology, Amrita University
Development of a Phototrophic Microbial Fuel Cell with sacrificial electrodes and a novel proton exchange matrix
If micro organisms can solve Sudoku and possibly have feelings, who is to say that they cannot also solve the planet’s energy crisis? Mr. Madhavan employs micro organisms to produce energy using microbial fuel cell (MFC). Micro organisms go through a series of cycles and pathways in order to survive, including the Electron Transport Pathway (ETP) in which bacteria release electrons which can be tapped as energy. In a two-chambered MFC, micro organisms interact with an anode in one chamber and in the presence of an oxidizing agent in the cathodic chamber scavenges electrons from the cathode. The two chambers are connected by an external circuit and connected to a load. In between the two chambers is a proton exchange membrane (PEM) which transports protons from the second chamber to the first and acts as a barrier for electrons. Therefore, a renewable source of energy can be maintained by just providing your bacterial culture with the proper nutrients to thrive and remain happy and satisfied (assuming they have emotions).
Mr. Madhavan has done extensive work on such MFCs and has experimented with various micro organisms and substrates to achieve high energy production. The phototropic MFC Mr. Madhavan designed using Synechococcus elongates using waste water as a substrate was able to generate approximately 10 mȦ and 1 volt of electricity. Other research in this area has even shown that using human urine can be used as a substrate for certain bacteria to produce enough energy to charge a mobile phone.
Although this microbial technology seems to be the “next big thing” (despite their small size) when it comes to renewable energy sources there is still a lot of work to be done before these bacteria batteries hit the market. As of now the MFCs are still much less efficient than solar cells and the search for the perfect bacteria and substrate continues.
Kal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA
A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry
With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways. Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists. As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery. While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories. Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site. As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty. In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases. We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase. We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.
Lalitha Subramanian, Ph.D.
Chief Scientific Officer & VP, Services at Scienomics, USA
Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery
Lalitha Subramanian, Dora Spyriouni, Andreas Bick, Sabine Schweizer, and Xenophon Krokidis Scienomics
The discovery of a compound which is potent in activity against a target is a major milestone in Pharmaceutical and Biotech industry. However, a potent compound is only effective as a therapeutic agent when it can be administered such that the optimal quantity is transported to the site of action at an optimal rate. The active pharmaceutical ingredient (API) has to be tested for its physicochemical properties before the appropriate dosage form and formulation can be designed. Some of the commonly evaluated parameters are crystal forms and polymorphs, solubility, dissolution behavior, stability, partition coefficient, water sorption behavior, surface properties, particle size and shape, etc. Pharmaceutical development teams face the challenge of quickly and efficiently determining a number of properties with small quantities of the expensive candidate compounds. Recently the trend has been to screen these properties as early as possible and often the candidate compounds are not available in sufficient quantities. Increasingly, these teams are leveraging nanoscale simulations similar to those employed by drug discovery teams for several decades. Nanoscale simulations are used to predict the behavior using very little experimental data and only if this is promising further experiments are done. Another aspect where nanoscale simulations are being used in drug development and drug delivery is to get insights into the behavior of the system so that process failures can be remediated and formulation performance can be improved. Thus, the predictive screening and the in-depth understanding leads to experimental efficiency resulting in far-reaching business impacts.
With specific examples, this talk will focus on the different types of nanoscale simulations used to predict properties of the API in excipients and also provide insight into system behavior as a function of shelf life, temperature, mechanical stress, etc.