Drug development aims to bring to market active pharmaceutical ingredients (APIs) identified during the drug discovery process. During this phase, the drugs undergo many tests to fully characterize their physical and chemical properties. Risk assessment is really what this is all about! It is less risky for pharmaceutical companies to assess potential development issues earlier rather than later (e.g., polymorphism, solubility, stability) in order to assess the manufacturability of the actives.Researchers typically don't have a large quantity of material to work with early in development. For this reason, in-silico methods such as those deployed within the BIOVIA Materials Studio?? modelling and simulation environment are very valuable. Materials Studio enables researchers to predict and understand the relationship between a material's molecular structure and its properties and/or behaviour.Amongst all stability stress tests performed, degradation is certainly one of the top issues pharmaceutical research scientists need to assess early by focusing on a number of possible API degradation mechanisms, including autoxidation (or hydrogen abstraction). The reaction between the pharmaceutical compounds and molecular oxygen can initiate an oxidative chain reaction that can lead to the reduction of the therapeutic agent, formation of toxic products, decreased bioavailability and other degradation processes. A research paper published this summer describes an in silico method for estimating bond dissociation energies based on the Density Functional Theory (DFT), a well-known computational chemistry tool for performing electronic structure calculations. The results can be used to assess the propensity of a drug substance with respect to autoxidation.The method was built using the BIOVIA Pipeline Pilot?? scientific workflow authoring application (and in particular the Pipeline Pilot Materials Studio Collection) to automate all the required quantum mechanical calculations. All steps of the workflow including jobs submission, execution on a remote cluster and analysis were fully automated using Pipeline Pilot. The calculations were conducted via a secure web interface allowing for efficient analysis, reporting and sharing of results.Initially, the method was validated against a set of 45 molecules and subsequently applied to APIs with known degradation history. Eventually, the risk assessment shown in the following figure was obtained.In conclusion, such Bond Dissociation Energy (BDE) calculations may be taken as a complementary source of information with experimental stress testing for early compound stability profiling. In addition, the authors reported that 'there is no need to be a highly skilled computational expert to use the Pipeline Pilot protocol.'Predicting Drug Substances AutoxidationLienard P, Gavartin J, Boccardi G, Meunier M.Pharm Res. 2014 Aug 13
Offering insight from the perspective of a Pipeline Pilot and Materials Studio user, Accelrys is pleased to host a posting written by guest blogger Dr. Misbah Sarwar, Research Scientist at Johnson Matthey. Dr. Sarwar recently completed a collaboration project focused on fuel cell catalyst discovery and will share her results in an upcoming webinar. This post provides a sneak peek into her findings..."In recent years there has been a lot of interest in fuel cells as a "green" power source in the future, particularly for use in cars, which could revolutionize the way we travel. A (Proton Exchange Membrane) fuel cell uses hydrogen as a fuel source and oxygen (from air), which react to produce water and electricity. However, we are still some time away from driving fuel cell cars, as there are many issues that need to be overcome for this technology to become commercially viable. These include improving the stability and reactivity of the catalyst as well as lowering their cost, which can potentially be achieved by alloying, but identifying the correct combinations and ratios of metals is key. This is a huge task as there are potentially thousands of different combinations and one where modeling can play a crucial role.As part of the iCatDesign project, a three-year collaboration with Accelrys and CMR Fuel Cells funded by the UK Technology Strategy Board, we screened hundreds of metal combinations using plane wave CASTEP calculations.In terms of stability, understanding the surface composition in the fuel cell environment is key. Predicting activity usually involves calculating barriers to each of the steps in the reaction, which is extremely time consuming and not really suited to a screening approach. Could we avoid these calculations and predict the activity of the catalyst based on adsorption energies or some fundamental surface property? Of course these predictions would have to be validated and alongside the modeling work, an experimental team at JM worked on synthesizing, characterizing and testing the catalysts for stability and activity.The prospect of setting up the hundreds of calculations, monitoring these and then analyzing the results seemed to us to be quite daunting and it was clear that some automation was required to both set up the calculations and process the results quickly. Using Pipeline Pilot technology (now part of Materials Studio Collection) protocols were developed which processed the calculations and statistical analysis tools developed to establish correlations between materials composition, stability and reactivity. The results are available to all partners through a customized web-interface.The protocols have been invaluable as data can be processed at the click of a button and customized charts produced in seconds. The timesaving is immense, saving days of endless copying, pasting and manipulating data in spreadsheets, not to mention minimizing human error, leaving us to do the more interesting task of thinking about the science behind the results. I look forward to sharing these results and describing the tools used to obtain them in more detail in the webinar, Fuel Cell Catalyst Discovery with the Materials Studio Collection, on 21st July."
Informatics in High Content Screening (HCS) is reshaping the mix of scientists driving drug discovery efforts. In the early days of HCS I worked closely with electrical, mechanical and software engineers to develop better systems for image acquisition and processing. My responsibilities as an HCS biologist involved painstaking hours of sample preparation and cell cultures and constant enhancements to my materials and methods section for preparing my biological specimens for imaging. I was motivated by the many new collaborative efforts that beganwith the software engineers, the systems engineers and the machine vision scientist developing HCS systems. I found myself teaching basic concepts of biology as I learned about illumination and optics, piezoelectric drives for auto focusing and, of course, the strings of zeros and ones that would eventually tell me what happened to my protein. It was exciting for me to be part of a cross functional team developing new applications by piecing together advances in hardware, image processing and biological assay technologies.High Content Screening systems and vendor software has come along way since my introduction to the technology ten years ago. Vendors struggled between giving end users powerful, flexible systems and ease of use (1). The bottleneck has shifted from application development to data informatics . Software systems in HCS have evolved to integrate databases and other related sources for chemical structures, target characteristics, and assay results. Today, Icollaborate with colleaguesin HCSin new areas that include data mining, principal component analysis, Bayesian modeling, decision trees, and data management.The mix of HCS conference speakers and attendees has shifted from what had primarily been assay developers to a growing population of informaticians and IT experts. Talks have moved beyond assay design and system development to incorporate more downstream data processing. We have worked on complex fingerprinting methods for predicting characteristics of a compound for such things as predicting mechanism of action or how it might affect a particular biological pathway involved for example, in neuronal stem cell differentiation. Vendors are moving to more open systems for image processing and are integrating more third party applications into their HCS acquisition systems to keep up with the shifting bottlenecks and emerging solutions. Informaticians have been able to improve data analysis efforts and significantly reduce the number of man-hours required for downstream data analysis (2). I've been fortunate in having been able to develop relationships with experts at most of the leading HCS instrument companies. My journey has been one of constant growth and continuous learning. I"m anxious to know what"s coming next in High Content Screening and eager to learn from my ever growing network of scientific experts.1. High-Content Analysis: Balancing Power and Ease of Use by Jim Kling2. Data Analysis For A High Content Assay Using Pipeline Pilot": 8x Reduction in Manhours from a poster by L. Bleicher, Brain Cells Inc
Next Generation Sequencing produces huge quantities of data,currently up to 60 million sequences per file. Algorithms used to analyse these data load all the information from one file into computer memory in order to process it. With the growth in data volumes these algorithms are beginning to slow down. This is a problem noted for algorithms which detect new forms of RNA and quantify them in RNA sequencing experiments.In his talk at the 'High Throughtput Sequencing Special Interest Group' (HitSIG) Adam Roberts from Berkeley, CA discussed his new online algorithm 'EXPRESS', designed to interpret RNA sequencing data. (Roberts and Pachter, 2011 in press, Bioinformatics).Online algorithms process data arriving in real-time. The models generated are updated a sequence at a time. Therefore, the amount of memory required stays constant whatever the volume of data processed and there is no need to save the data if it will not be analysed again later.Online algorithms would fit very naturally in Pipeline Pilot data piplines. They would also fit well with the new real-time sequencing technologies such as the Oxford Nanopore GridION system. The GridION system already uses Pipeline Pilot to control it's 'Run until ..sufficient' workflows.Bringing all three technologies together would allow data interpretation to be generated directly from the sequencing machine and the flood of data could be directed straight into the most useful channels.Fig 1 an RNA sequencing experiment showing a known and a newly discovered form of RNA and the depth of the sequences used to identify them, along one region of the mouse genome.
Antibodies are increasingly important in medical diagnostics and in the treatment of a broad range of disease states including cancer, inflammation and auto-immune diseases. Through antibody drug conjugates (ADCs), antibodies also enable the targeted delivery of traditional drugs. In contrast to traditional chemotherapeutic agents, ADCs target and attack the cancer cells so that healthy cells are less severely affected.Many critical properties of antibodies cannot be predicted from sequence alone and so building computational models is seen as a cost-effective aid in antibody design, maturation and formulation processes. Specifically, some important properties that can be estimated from structural models are antigen affinity (avidity), stability and aggregation propensity. Due to the high structural similarity between different antibodies and recently improved methods, structure prediction has been shown to give accurate antibody structure models.The second Antibody Modeling Assessment (AMA-II) is a community-wide experiment organized by Pfizer and Janssen R&D in 2013 to assess the state of the art in antibody structure modeling. The participants included prediction teams from Dassault Syst??mes BIOVIA (previously Accelrys), Chemical Computing Group, the Jeffrey Gray Lab at Johns Hopkins (Rosetta Antibody), Hiroki Shirai and Astellas Pharma, Inc., Macromoltek, Schr??dinger and the fully automated PIGS (Prediction of Immunoglobulin Structure) server. The goal was to predict the 3D structure of 11 antibody variable domain targets given their amino acid sequence. The targets covered different antigen binding site conformations and represented a variety of species including human, mouse and rabbit. It was a blind-prediction study. The participants did not know the actual X-ray structures during the prediction phase.Our team consisting of Marc Fasnacht, Ken Butenhof, Anne Goupil, Francisco Hernandez-Guzman, Hongwei Huang and Lisa Yan has recently published a paper entitled "Automated Antibody Structure Prediction using Accelrys Tools: Results and Best Practices" in "Proteins: Structure, Function and Bioinformatics" (Wiley Online Library). The main BIOVIA (previously Accelrys) tool used in the study was the Discovery Studio life sciences modeling and simulation application.During the prediction phase, each team member chose to build their targets using either a single, chimeric or multiple template approach for the framework region. Then the hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. The templates were selected by Discovery Studio, but with flexibility of human intervention. In the post-analysis phase, the team carried out a systematic study of the modeling methods employed during the "blind phase" for framework modeling, e.g. single, chimeric and multiple template approaches using a fully automated template selection process. In addition, they also examined the factors affecting the refinement of the complementary determining regions (CDRs).The analysis of the models shows that Discovery Studio enables the construction of accurate models for the framework and the canonical CDR regions, with backbone root-mean-square deviations (RMSDs) from the X-ray structures on average below 1 ?? for most of the regions. The fully automated multiple-template approach matched or exceeded the best manual results. The antibody model assessment shows that the submitted models are high quality, with local geometry assessment scores similar to those of the target X-ray structures.Our team"s approach as published in Proteins demonstrates the reliability of automated template selection for framework and CDR regions based on a curated database. The automated methods in our study generate models as good as or better than those with manual intervention. You can access the paper here: "Automated Antibody Structure Prediction using Accelrys Tools: Results and Best Practices"
I started the Thanksgiving Week thankful that it was a short week. It needed to be short to recover from the action packed agenda at the 2009 Chemical and Biological Science and Technology Conference in Dallas the week prior. The conference was a huge success on many different levels. Although, the conference has been ongoing for the last few years, this was a first time combination of physical science and medicinal disciplines and to the credit of the conference organizers, it was well done! There were over 1400 people in attendance with over 600 poster presentations and countless oral presentations; however, even with the number of people and logistical challenges that existed, this was one of the best events I have attended (and I have attended a few).I enjoyed the conference from the perspective that I was able to connect with former colleagues and make new friends (accomplices). However, the most important aspect for me was hearing about some of the great work that is going into making our country safer. The science and technology is cutting edge and driving innovation in so many different disciplines.As mentioned in a previous post we were honored to present two posters;Nancy Miller Latimer presented a poster on "Using Data Pipelining to Analyze Biological Threats: A Biomarker Case Study".AndDr. Nick Reynolds presented a poster on "Applications of nanoscale simulations methods for understanding the structure and mechanisms of chemical sensors".Both posters were well received and very applicable to the technology challenges that we face; Dr Reynolds and Ms. Miller Latimer directed and managed the traffic expertly (and there were a lot of people at the presentation).Over the past few conferences that I have been to, data management and integration is becoming an increasing concern to all the Federal Agencies as more and more data intensive programs come into existence. From new drug and vaccine discovery to biometrics, the data produced for use and reuse is overwhelming legacy systems and there is increasing focus on how to address this challenge.Addressing this challenge and back to Ms. Miller Latimer"s discussion on Data Pipelining;She demonstrated "data pipelining", using Pipeline Pilot", in a biomarker case study for ALI (Acute Lung Injury). As part of this analysis, Pipeline Pilot was used to analyze and integrate mass spec proteomics data with gene expression data and sequences using data pipelining. Additionally, this study also showed how to automatically mine the literature analysis results for differentially expressed genes/proteins and then publish enterprise-wide interactive solutions via web portals.To underscore the interest in this integrative and flexible capability, Ms. Miller Latimer"s work was recognized as the best poster overall (over 600 poster were presented). I was proud to be there as she received the award from Colonel Michael O"Keefe, Deputy Director, Chemical/Biological Technologies, Defense Threat Reduction Agency. Accelrys is proud of Ms. Miller Latimer"s contribution. Well Done!!
Accelrys has recently concluded a series of meetings with a specially convened Biological Registration Special Interest Group , (SIG), formed between several major pharmaceutical companies and Accelrys. The objective of this forum was to understand some of the critical market and product requirements needed in order to build a state-of-the-art Biologics Registration system.The success of the SIG can be attributed to the customer members being very open towards one another, in spite of being competitors, and the tremendous diligence each company put into specifying user requirements. This open and collaborative approach to software development has become an innovative way to introduce first of a kind technology into the market.First of a kind software is usually developed as a bespoke project for a single company and then modified over time to meet the needs of the wider market. This can create disadvantages for early adopters as the product functionality evolves and improves with subsequent releases. This situation can be avoided by getting a wider set of requirements through a collaborative SIG formed of a diverse and representative sample of interested parties.The ability to capture and prioritize a wider set of requirements through leading companies discussing and debating the relative merits and benefits of proposed features, is a more efficient and effective way of understanding market requirements than more traditional methods. The approach also enables the development team to capture feedback and more rapidly create a product that should be attractive to the wider market. The anticipated result is the timely delivery of a product that is well positioned to capture both broad interest and market share.Have you innovated through collaborative work groups? If so, we would welcome the chance to learn from your experience.