Advanced Chemistry Development

Toronto, Canada

Advanced Chemistry Development

Toronto, Canada
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Moser A.,Advanced Chemistry Development | Pautler B.G.,Advanced Chemistry Development
Magnetic Resonance in Chemistry | Year: 2016

The successful elucidation of an unknown compound’s molecular structure often requires an analyst with profound knowledge and experience of advanced spectroscopic techniques, such as Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. The implementation of Computer-Assisted Structure Elucidation (CASE) software in solving for unknown structures, such as isolated natural products and/or reaction impurities, can serve both as elucidation and teaching tools. As such, the introduction of CASE software with 112 exercises to train students in conjunction with the traditional pen and paper approach will strengthen their overall understanding of solving unknowns and explore of various structural end points to determine the validity of the results quickly. © 2016 John Wiley & Sons, Ltd.

This report studies the Global Biosimulation Market, analyzes and researches the Biosimulation development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like Certara Simulation Plus Dassault Systèmes Schr?dinger Advanced Chemistry Development Chemical Computing Group Entelos Genedata Ag Physiomics PLC Rhenovia Pharma Market segment by Application, Biosimulation can be split into Application 1 Application 2 Application 3 United States, EU, Japan, China, India and Southeast Asia Biosimulation Market Size, Status and Forecast 2021 1 Industry Overview of Biosimulation 1.1 Biosimulation Market Overview 1.1.1 Biosimulation Product Scope 1.1.2 Market Status and Outlook 1.2 Global Biosimulation Market Size and Analysis by Regions 1.2.1 United States 1.2.2 EU 1.2.3 Japan 1.2.4 China 1.2.5 India 1.2.6 Southeast Asia 1.3 Biosimulation Market by End Users/Application 1.3.1 Application 1 1.3.2 Application 2 1.3.3 Application 3 2 Global Biosimulation Competition Analysis by Players 2.1 Biosimulation Market Size (Value) by Players (2015-2016) 2.2 Competitive Status and Trend 2.2.1 Market Concentration Rate 2.2.2 Product/Service Differences 2.2.3 New Entrants 2.2.4 The Technology Trends in Future 3 Company (Top Players) Profiles 3.1 Certara 3.1.1 Company Profile 3.1.2 Main Business/Business Overview 3.1.3 Products, Services and Solutions 3.1.4 Biosimulation Revenue (Value) (2011-2016) 3.1.5 Recent Developments 3.2 Simulation Plus 3.2.1 Company Profile 3.2.2 Main Business/Business Overview 3.2.3 Products, Services and Solutions 3.2.4 Biosimulation Revenue (Value) (2011-2016) 3.2.5 Recent Developments 3.3 Dassault Systèmes 3.3.1 Company Profile 3.3.2 Main Business/Business Overview 3.3.3 Products, Services and Solutions 3.3.4 Biosimulation Revenue (Value) (2011-2016) 3.3.5 Recent Developments 3.4 Schr?dinger 3.4.1 Company Profile 3.4.2 Main Business/Business Overview 3.4.3 Products, Services and Solutions 3.4.4 Biosimulation Revenue (Value) (2011-2016) 3.4.5 Recent Developments 3.5 Advanced Chemistry Development 3.5.1 Company Profile 3.5.2 Main Business/Business Overview 3.5.3 Products, Services and Solutions 3.5.4 Biosimulation Revenue (Value) (2011-2016) 3.5.5 Recent Developments 3.6 Chemical Computing Group 3.6.1 Company Profile 3.6.2 Main Business/Business Overview 3.6.3 Products, Services and Solutions 3.6.4 Biosimulation Revenue (Value) (2011-2016) 3.6.5 Recent Developments 3.7 Entelos 3.7.1 Company Profile 3.7.2 Main Business/Business Overview 3.7.3 Products, Services and Solutions 3.7.4 Biosimulation Revenue (Value) (2011-2016) 3.7.5 Recent Developments 3.8 Genedata Ag 3.8.1 Company Profile 3.8.2 Main Business/Business Overview 3.8.3 Products, Services and Solutions 3.8.4 Biosimulation Revenue (Value) (2011-2016) 3.8.5 Recent Developments 3.9 Physiomics PLC 3.9.1 Company Profile 3.9.2 Main Business/Business Overview 3.9.3 Products, Services and Solutions 3.9.4 Biosimulation Revenue (Value) (2011-2016) 3.9.5 Recent Developments 3.10 Rhenovia Pharma 3.10.1 Company Profile 3.10.2 Main Business/Business Overview 3.10.3 Products, Services and Solutions 3.10.4 Biosimulation Revenue (Value) (2011-2016) 3.10.5 Recent Developments For more information or any query mail at [email protected]

Elyashberg M.,Advanced Chemistry Development | Blinov K.,Advanced Chemistry Development | Smurnyy Y.,Advanced Chemistry Development | Churanova T.,Advanced Chemistry Development | Williams A.,Royal Society of Chemistry
Magnetic Resonance in Chemistry | Year: 2010

The accuracy of 13C chemical shift prediction by both DFT GIAO quantum-mechanical (QM) and empirical methods was compared using 205 structures for which experimental and QM-calculated chemical shifts were published in the literature. For these structures, 13C chemical shifts were calculated using HOSE code and neural network (NN) algorithms developed within our laboratory. In total, 2531 chemical shifts were analyzed and statistically processed. It has been shown that, in general, QM methods are capable of providing similar but inferior accuracy to the empirical approaches, but quite frequently they give larger mean average error values. For the structural set examined in this work, the following mean absolute errors (MAEs) were found: MAE(HOSE) = 1.58 ppm, MAE(NN) = 1.91 ppm and MAE(QM) = 3.29 ppm. A strategy of combined application of both the empirical and DFT GIAO approaches is suggested. The strategy could provide a synergistic effect if the advantages intrinsic to each method are exploited. Copyright © 2010 John Wiley & Sons, Ltd.

Elyashberg M.,Advanced Chemistry Development | Williams A.J.,Royal Society of Chemistry | Blinov K.,Advanced Chemistry Development
Natural Product Reports | Year: 2010

It is shown in this review that the application of an expert system for the purpose of computer-assisted structure elucidation allows the researcher to avoid the production of incorrect structural hypotheses, and also to evaluate the reliability of suggested structures. Many examples of structure revision using CASE methods are given. © 2010 The Royal Society of Chemistry.

Elyashberg M.,Advanced Chemistry Development | Blinov K.,Advanced Chemistry Development | Molodtsov S.,RAS Institute of Organic Chemistry | Williams A.,Royal Society of Chemistry
Magnetic Resonance in Chemistry | Year: 2012

Structure elucidation using 2D NMR data and application of traditional methods of structure elucidation are known to fail for certain problems. In this work, it is shown that computer-assisted structure elucidation methods are capable of solving such problems. We conclude that it is now impossible to evaluate the capabilities of novel NMR experimental techniques in isolation from expert systems developed for processing fuzzy, incomplete and contradictory information obtained from 2D NMR spectra. © 2012 John Wiley & Sons, Ltd.

News Article | December 14, 2015

The articles “The Future-as-a-Service” and “Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World” nicely lay out the informatics challenges associated with dealing with LIVE data (raw) versus DEAD1 data (processed) when it comes to the externalization of scientific research and development. As their authors point out, there are a multitude of less-than-adequate solutions that can be applied to this problem. One of the biggest contributors to the problem is how each business or portion of a business enters into a relationship with an external CRO partner. Most, if not all of the time, the business driver is reactive, functional and financial, rather than strategic. For example, a small firm may have valuable IP but not the funding or time to set up their own in-house R&D laboratories, or large more well-established companies may be looking for ways to reduce costs and/or grow earnings. Neither of these situations creates environments in which a holistic approach to managing external R&D relationships are easily supported, nurtured and grown. Having a well-thought-out strategic sourcing model allows a company to fully integrate internal human and capital resources within the context and framework of effective utilization of external resources. This can lead to improved FTE hiring and capital acquisition decisions. An added benefit is that CRO partners, or potential partners, are more easily able to identify where they may fit into the innovator’s business model, allowing them to better focus their resources and investments to meet the innovator’s R&D goals. In this article, we describe a model in which R&D organizations develop a sourcing strategy that is aligned with how they add value to their customers and shareholders. Many businesses understand this at a high level, but have failed to translate it to their business operations. Within the scope of this document, we limit our discussion to analytical-based activities, but this model can be applied to all functions within an R&D organization and into manufacturing. There are significant benefits to an entire organization using a single model, as it ensures alignment between strategic priorities across departments. At the end of this article, we will describe how the application of our strategic sourcing model has enabled a mutually beneficial three-way partnership that enables LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO. We define “strategic” as having two categories: For material generation functions of a company, strategic definition number 1 usually dominates, whereas for analytical functions, strategic definition number 2 usually dominates. Too often, companies decide to outsource work that is considered “routine.” We avoid using this term, as by its very nature it leads people within the organization to consider work that is described as routine as less important and, therefore, less valuable, which by definition limits people’s willingness to invest in a sustainable solution to support it. We suggest that work that is considered less important or less valuable simply should not be done by either the innovator or their partners. Companies should limit their efforts to work that is both important and critical to the company’s efforts. Within the definition of “important” there are two sub-categories: By our definition, all urgent work is critical to the company’s strategic efforts, as the urgency implies that other functions are waiting for the data to make decisions. As a result, all important and urgent work should be done internally (Figure 1, Quadrant 1), if possible. When this is not possible, doing this work at a CRO but inside an FTE team is the next best option (Figure 1, Quadrant 2). This solution limits the number of people who are exposed to the work product and, further, the priorities of an FTE team can be quickly adjusted in concert with the needs of the material generation functions. In the “Important and Not Urgent” category there are two sub-categories: Predictable is defined as worked that is governed by a qualified or validated method and a protocol. The qualified or validated method ensures that the method will produce meaningful results in a reliable manner, and the protocol defines how much work there is and when it needs to be executed or completed. These two attributes make the work product predictable, but not necessarily easy (Figure 1, Quadrant 3). The last category of work we have defined as “niche” (Figure 1, Quadrant 4). In our model, “niche” work is usually very important, but seldom strategic, and is often governed or mandated by industry regulations. Examples of work in this category are extractable and leachable or trace metals analysis. In this category, the innovator is specifically leveraging the technical expertise of CROs. These niche disciplines may require specific skill sets for which an innovator only has occasional need, whereas a CRO can aggregate need from several innovators to support permanent staff. Once a company has developed good agreement of what are strategic, predictable and niche work activities, a thoughtful approach toward outsourcing can be applied. We have taken a four-quadrant model approach that categorizes work activities while accepting the reality that, most of the time, the 80/20 rule applies, and the lines/interfaces between categories/quadrants can become blurred. Having a well-articulated sourcing model allows a company to agree on a standardized language — agreeing that only important work should be performed whether it is strategic, predictable or niche. This improves predictability for all involved by setting clear expectations for work done internally, and clarifies the role of outsourcing. Defining exactly what work should be externalized also eliminates the need for “approval” for every outsourcing decision. This has the major advantage of enabling a company to accurately and openly articulate its sourcing strategy to CROs, allowing CROs to tailor their customer outreach for what business might be available and to remove focus from what will not be available. In our model, work activities above the horizontal line can be actively prioritized by the innovator, whereas below-the-horizontal-line work is scheduled and prioritized by the CRO within the context of the associated business agreements. This is why unit work activity below the line is less expensive than similar corresponding work above the line. Predictable, non-strategic work is ideal for outsourcing. In general CROs set up their business to work in the predictable space. This is necessary so they can properly bid on work proposals. As soon as the work becomes too unpredictable, it becomes better-suited for a CRO FTE team, or an in-house FTE effort. However, through the application of this model, companies can significantly influence and change the CRO business through active engagement, partnership, and training of the CRO staff. Our approach allows for innovators and CROs to explore the natural migration of strategic work from Quadrant 1 that was once strategic, counterclockwise through the FTE team (Quadrant 2) and, ultimately, into the predictable space (Quadrant 3), or clockwise from strategic work (Quadrant 1) into the niche space (Quadrant 4) and again, ultimately, into the predictable space (Quadrant 3). Recently, our industry has observed migrations from the strategic (Quadrant 1) in both clockwise and counterclockwise directions for metals analysis and extractable and leachable analysis. Ultimately, having a well-defined model that drives outsourcing decisions allows for mutually beneficial investments by both the innovator, as well as the CRO. Even the best strategic sourcing model is significantly hampered by the difficulty innovators have getting access to all the LIVE data that a CRO generates on their behalf. To this end, we have recently embarked on collaboration with key partners to invest in data handling solutions that will enable LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO. Enabling the immediate and seamless transfer of the LIVE raw data allows us to further reconsider the current boundaries for what work can be performed at a CRO. Automated transfer of data from the CRO network provides key advantages, the absence of which can lead to hesitancy towards adapting a sourcing model like the one described in Figure 1. The first advantage is the elimination of innovator FTE time spent delivering numerical results to sample generating groups. This decreases the turnaround time for delivery of results which may be used to make decisions, examine trending, and complete regulatory filings. Although companies may utilize different software to report these numbers, creating a translation and delivery mechanism as part of the automated data delivery process is generally a simple customization. Secondly, the automated transfer enables delivery of sample and instrument metadata. Any recorded information regarding the experiments completed can be categorized and delivered to the innovator’s systems. For example, metadata such as the instrument preventative maintenance status can be accessed by the innovator as if it had been collected in the innovators laboratory. A third key advantage is providing the innovator access to LIVE raw data. This provides the innovator with several important functionalities. It enables the innovator to dig deeper into the data to answer questions. For example, “have we seen this peak before?”, or in a pre-approval inspection a regulator could ask, “can you show us the raw data?” It also allows the presentation of data collected internally and externally to be consolidated. Overlays for a comparability reports can easily be constructed with data from internal analytical functions and CROs together. The innovator is able to subject CRO data to the same disaster recovery and redundancy standards that it would for internally collected data. Some innovators have engineered solutions to allow a CRO to deliver data remotely into their internal systems. This arrangement achieves similar endpoints; however, it may not be a sustainable solution. Granting access to innovator systems requires a significant investment in a specific CRO, and thus could impinge on a competitive environment to address CRO capacity or performance issues. Further, it requires innovator-specific training, software licenses, and IT support. Those costs are typically the burden of the CRO. Creating a sustainable, expandable CRO-innovator data automation process requires two major considerations. The first hurdle is to address the wide range of data types deriving from the laboratories of a CRO and an Innovator. Even for analytical data collected internally at most innovators, there is no universal and sustainable solution for viewing and processing current and historical data. For a single discipline of analytical methods, there are likely several instrument and software vendors, each providing a proprietary data format. Extending your network to analytical labs in the CRO network further increases the variety of instrument and software vendors used. These inconsistencies can be overcome by utilizing pioneering software solutions, such as the ACD/Spectrus Platform (Advanced Chemistry Development, Toronto), which can view, process and store data collected on instruments from most leading analytical vendors. Further, the Allotrope Foundation aims to create a universal data format to address this serious business need, already recruiting a significant coalition of industry leaders to drive change in analytical data standards. These types of solutions are able to accommodate the many data formats that may be delivered from the CRO network. Importantly, the movement of data between CRO and innovator systems must be automated with minimal FTE effort. This can be achieved via several technological means. Solutions are available from vendors, such as BioVia, that physically shepherd the data between locations. One major concern from CROs is the access that each innovator has to their internal systems, which contain data from all of their clients. To address this, an FTP (file transfer protocol) site can be used as a neutral intermediary, allowing the CRO to deliver exactly the files it wants to send (Figure 2). This solution also allows customization of the cadence of data delivery. For example, the data can be uploaded as soon as it is collected, processed or fully reviewed. On the other side of the solution, the innovator can automate the delivery, processing, and archival of the files using a simple periodic sweep of the FTP site. Once the data is housed internally, the delivery of relevant data to internal systems is completed using an automation routine. For CROs, where the data itself is their product, their business process stresses consistency and standardization in data reporting. This makes automated delivery of data, such as numerical endpoints to a LIMS system, a sustainable automation requiring only infrequent lifecycle management. Once file-naming and metadata standards are set for the first participating CRO, the requirements for data sharing and delivery can be built directly into the RFP for the work. By incorporating these considerations into a data-sharing solution, the access to data collected internally or at a CRO are truly on the same playing field. This equality enables innovators to judiciously apply the Analytical Sourcing model described above. A robust and well-articulated sourcing strategy coupled with a seamless way to transfer LIVE data between CROs and innovators allows innovators to define CROs in the context of “partners.” This paradigm enables conversations to focus more around what work should be performed internally and what work should be performed at the CRO. This paradigm can enable both the innovator and CRO to efficiently utilize resources to meet their long-term strategic goals. Brian Fahie is Director, Technical Development and Evan Guggenheim is Scientist II, Technical Development, at Biogen. They may be reached at

Martin G.E.,Merck And Co. | Hilton B.D.,Merck And Co. | Blinov K.A.,Advanced Chemistry Development
Magnetic Resonance in Chemistry | Year: 2011

Various experimental methods have been developed to unequivocally identify vicinal neighbor carbon atoms. Variants of the HMBC experiment intended for this purpose have included 2J3J-HMBC and H2BC. The 1,1-ADEQUATE experiment, in contrast, was developed to accomplish the same goal but relies on the 1JCC coupling between a proton-carbon resonant pair and the adjacent neighbor carbon. Hence, 1,1-ADEQUATE can identify non-protonated adjacent neighbor carbons, whereas the 2J3J-HMBC and H2BC experiments require both neighbor carbons to be protonated to operate. Since 1,1-ADEQUATE data are normally interpreted with close reference to an HSQC spectrum of the molecule in question, we were interested in exploring the unsymmetrical indirect covariance processing of multiplicity-edited GHSQC and 1,1-ADEQUATE spectra to afford an HSQC-ADEQUATE correlation spectrum that facilitates the extraction of carbon-carbon connectivity information. The HSQC-ADEQUATE spectrum of strychnine is shown and the means by which the carbon skeleton can be conveniently traced is discussed. Copyright © 2011 John Wiley & Sons, Ltd.

Martin G.E.,Merck And Co. | Hilton B.D.,Merck And Co. | Blinov K.A.,Advanced Chemistry Development
Journal of Natural Products | Year: 2011

1H- 13C GHSQC and GHMBC spectra are irrefutably among the most valuable 2D NMR experiments for the establishment of unknown chemical structures. However, the indeterminate nature of the length of the long-range coupling(s) observed via the nJ CH-optimized delay of the GHMBC experiment can complicate the interpretation of the data when dealing with novel chemical structures. A priori there is no way to differentiate 2J CH from nJ CH correlations, where n ≥ 3. Access to high-field spectrometers with cryogenic NMR probes brings 1,1- and 1,n-ADEQUATE experiments into range for modest samples. Subjecting ADEQUATE spectra to covariance processing with high sensitivity experiments such as multiplicity-edited GHSQC affords a diagonally symmetric 13C- 13C correlation spectrum in which correlation data are observed with the apparent sensitivity of the GHSQC spectrum. HSQC-1,1-ADEQUATE covariance spectra derived by co-processing of GHSQC and 1,1-ADEQUATE spectra allow the carbon skeleton of molecules to be conveniently traced. HSQC-1,n-ADEQUATE spectra provide enhanced access to correlations equivalent to 4J CH correlations in a GHMBC spectrum. When these data are used to supplement GHMBC data, a powerfully synergistic set of heteronuclear correlations are available. The methods discussed are illustrated using retrorsine (1) as a model compound. © 2011 The American Chemical Society and American Society of Pharmacognosy.

Elyashberg M.,Advanced Chemistry Development
TrAC - Trends in Analytical Chemistry | Year: 2015

The state of the art and recent developments in application of nuclear magnetic resonance (NMR) for structure elucidation and identification of small organic molecules are discussed. The recently suggested new two-dimensional (2D)-NMR experiments combined with the advanced instrumentation allow structure elucidation of new organic compounds at a sample amount of less than 10μg. A pure shift approach that provides 1H-decoupled proton spectra drastically simplified 1H and 2D NMR spectra interpretation. The structure elucidation of extremely hydrogen-deficient compounds was dramatically facilitated due to the methodology based on combination of new 2D-NMR experiments providing long-range heteronuclear correlations with computer-assisted structure elucidation (CASE). The capabilities of CASE systems are discussed. The role of NMR-spectrum prediction in structure verification and NMR approaches for qualitative mixture analysis are considered. © 2015 Elsevier B.V.

News Article | September 27, 2016

ACD/Labs’ MetaSense is an automated metabolite identification platform that combines comprehensive metabolic transformation prediction with efficient analysis of LCMS analytical measurements to identify, visualize and report chemical biotransformations. Built on the ACD/Spectrus Platform, MetaSense offers knowledge management capabilities that allow the information gained in metabolite studies to be applied to other areas of R&D. The live data environment and web-based visualization components facilitate decision-support and effective knowledge sharing throughout organizations and between partners. This allows discovery chemists to more easily drive synthetic directions to increase/decrease stability, for example, or help development scientists investigate the potential toxicity of parent compounds. The platform provides an array of functionality for analytical scientists that undertake detailed metabolism studies, including: native LCMS vendor format support, a fractional mass filter for untargeted metabolites, a metabolite prediction engine and the ability to fully control the manual processing of data. In terms of automation, the platform features configurable options to automate processing and interpretation of LCMS data, automatic tracking of parent and metabolites for stability studies and the automatic creation of live biotransformation maps from interpreted data. Advanced Chemistry Development, Inc., 800-304-3988

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