Driving Aspirational Process Mass Intensity Using SMART-PMI and Innovative Chemistry

An important metric for gauging the impact a synthetic route has on chemical resources, cost, and sustainability is process mass intensity (PMI). Calculating the overall PMI or step PMI for a given synthesis from a process description is more and more common across the industry. Our company has established a strong track record of delivering on our Corporate Sustainability goals, being recognized with seven EPA Green Chemistry Challenge Awards in the last 15 years. While green chemistry principles help in optimizing PMI and developing more sustainable processes, a key challenge for the field is defining what ‘good’ looks like for any given molecule. Predicting aspirational PMI for a synthetic target is not yet possible from chemical structure alone. The only tool chemists have at their disposal to predict PMI requires the synthetic route to be available, which is inherently retrospective. We have developed SMART-PMI (in-Silico MSD Aspiration-al Research Tool) to fill this glaring gap. Using only a 2D chemical structure, which enables a measure of molecular complexity, we can generate a predicted SMART-PMI using historical PMI data from our company’s clinical and commercial portfolio of processes. From this SMART-PMI prediction, we have established target ranges for Successful, World Class, and Aspirational PMI. Using this model, chemists can develop powerful synthetic strategies that make the biggest impact on PMI and, in turn, drive improvements to the model. The potential of SMART-PMI to set industry-wide aspirational PMI targets is discussed.

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Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

Journal of Diabetes Science and Technology ◽

10.1177/19322968211012392 ◽

2021 ◽

pp. 193229682110123

Author(s):

Chiara Roversi ◽

Martina Vettoretti ◽

Simone Del Favero ◽

Andrea Facchinetti ◽

Pratik Choudhary ◽

...

Keyword(s):

Type 1 Diabetes ◽

Glycemic Control ◽

In Silico ◽

Open Loop ◽

Random Errors ◽

Linear Regression Models ◽

Reference Case ◽

Counting Error ◽

The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

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Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

New Journal of Chemistry ◽

10.1039/d0nj05570k ◽

2021 ◽

Vol 45 (10) ◽

pp. 4756-4765

Author(s):

Daoxing Chen ◽

Liting Zhang ◽

Yanan Liu ◽

Jiali Song ◽

Jingwen Guo ◽

...

Keyword(s):

In Silico ◽

In Silico Study ◽

The Impact ◽

Insight Into

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

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Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

Journal of Personalized Medicine ◽

10.3390/jpm11020131 ◽

2021 ◽

Vol 11 (2) ◽

pp. 131

Author(s):

Laura B. Scheinfeldt ◽

Andrew Brangan ◽

Dara M. Kusic ◽

Sudhir Kumar ◽

Neda Gharani

Keyword(s):

In Silico ◽

Drug Efficacy ◽

Common Variant ◽

Genomic Research ◽

Whole Genome Sequencing Data ◽

Mendelian Disease ◽

Allele Frequency Distribution ◽

Sequencing Data ◽

Patient Race ◽

The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

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The Eukaryotic Promoter Database EPD: the impact of in silico primer extension

Nucleic Acids Research ◽

10.1093/nar/gkh122 ◽

2004 ◽

Vol 32 (90001) ◽

pp. 82D-85 ◽

Cited By ~ 48

Author(s):

C. D. Schmid

Keyword(s):

In Silico ◽

Primer Extension ◽

Eukaryotic Promoter ◽

Eukaryotic Promoter Database ◽

The Impact

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A Review of Bacteriochlorophyllides: Chemical Structures and Applications

Molecules ◽

10.3390/molecules26051293 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1293

Author(s):

Chih-Hui Yang ◽

Keng-Shiang Huang ◽

Yi-Ting Wang ◽

Jei-Fu Shaw

Keyword(s):

Solar Cell ◽

Energy Harvesting ◽

Chemical Structure ◽

The Other ◽

Dye Sensitized Solar Cell ◽

Synthetic Route ◽

Chemical Structures ◽

Dye Sensitized ◽

Heme Synthesis ◽

Potential Applications

Generally, bacteriochlorophyllides were responsible for the photosynthesis in bacteria. Seven types of bacteriochlorophyllides have been disclosed. Bacteriochlorophyllides a/b/g could be synthesized from divinyl chlorophyllide a. The other bacteriochlorophyllides c/d/e/f could be synthesized from chlorophyllide a. The chemical structure and synthetic route of bacteriochlorophyllides were summarized in this review. Furthermore, the potential applications of bacteriochlorophyllides in photosensitizers, immunosensors, influence on bacteriochlorophyll aggregation, dye-sensitized solar cell, heme synthesis and for light energy harvesting simulation were discussed.

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Influence of the Anthocyanin and Cofactor Structure on the Formation Efficiency of Naturally Derived Pyranoanthocyanins

International Journal of Molecular Sciences ◽

10.3390/ijms22136708 ◽

2021 ◽

Vol 22 (13) ◽

pp. 6708

Author(s):

Gonzalo Miyagusuku-Cruzado ◽

Danielle M. Voss ◽

M. Monica Giusti

Keyword(s):

Caffeic Acid ◽

Chemical Structure ◽

Sinapic Acid ◽

Molar Ratio ◽

Tandem Mass ◽

Limited Information ◽

Formation Efficiency ◽

The Impact ◽

And Storage ◽

H 20

Pyranoanthocyanins are anthocyanin-derived pigments with higher stability to pH and storage. However, their slow formation and scarcity in nature hinder their industrial application. Pyranoanthocyanin formation can be accelerated by selecting anthocyanin substitutions, cofactor concentrations, and temperature. Limited information is available on the impacts of the chemical structure of the cofactor and anthocyanin; therefore, we evaluated their impacts on pyranoanthocyanin formation efficiency under conditions reported as favorable for the reaction. Different cofactors were evaluated including pyruvic acid, acetone, and hydroxycinnamic acids (p-coumaric, caffeic, ferulic, and sinapic acid) by incubating them with anthocyanins in a molar ratio of 1:30 (anthocyanin:cofactor), pH 3.1, and 45 °C. The impact of the anthocyanin aglycone was evaluated by incubating delphinidin, cyanidin, petunidin, or malvidin derivatives with the most efficient cofactor (caffeic acid) under identical conditions. Pigments were identified using UHPLC-PDA and tandem mass spectrometry, and pyranoanthocyanin formation was monitored for up to 72 h. Pyranoanthocyanin yields were the highest with caffeic acid (~17% at 72 h, p < 0.05). When comparing anthocyanins, malvidin-3-O-glycosides yielded twice as many pyranoanthocyanins after 24 h (~20%, p < 0.01) as cyanidin-3-O-glycosides. Petunidin- and delphinidin-3-O-glycosides yielded <2% pyranoanthocyanins. This study demonstrated the importance of anthocyanin and cofactor selection in pyranoanthocyanin production.

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CEO behavior and sustainability performance: the moderating role of corporate governance

Property Management ◽

10.1108/pm-01-2021-0009 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Amel Kouaib ◽

Asma Bouzouitina ◽

Anis Jarboui

Keyword(s):

Corporate Governance ◽

Real Estate ◽

Corporate Sustainability ◽

Least Square ◽

Content Type ◽

Sustainability Performance ◽

Corporate Sustainability Performance ◽

Good Corporate Governance ◽

Governance Practices ◽

The Impact

PurposeThis paper explores how the tension between a firm's CEO overconfidence feature and externally observable hubris attribute may determine the level of corporate sustainability performance. This work also contemplates the impact of the moderator “corporate governance practices.”Design/methodology/approachThis study uses a sample of 658 firm-year-observations using a sample of European real estate firms indexed on Stoxx Europe 600 Index from 2006 to 2019. To test the developed hypotheses, feasible generalized least square (FGLS) regression is applied.FindingsFindings suggest that a good corporate governance score strengthens the positive effect of the psychological bias (CEO overconfidence) on corporate sustainability performance while it fails to attenuate the negative effect of the cognitive bias (CEO hubris).Research limitations/implicationsThe research provides an overview of the impact of CEO personality traits on the corporate sustainability performance level in the European real estate sup-sector. As corporate governance can have a major impact to control these traits, the authors recommend European real estate companies to improve their corporate governance practices.Originality/valueThis study contributes to the existent literature this gap with two empirical novelties: (1) providing a novel insight into sustainability involvement using a sample of European real estate sup-sector and (2) investigating the moderating effect on the link between CEO psychological and cognitive biases and sustainability performance. This study provides empirical evidence that entrenchment problems arising from CEO hubris would not be mitigated by a good corporate governance practice.

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Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach

Sustainability ◽

10.3390/su12114753 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4753

Author(s):

Viju Raghupathi ◽

Jie Ren ◽

Wullianallur Raghupathi

Keyword(s):

Machine Learning ◽

Corporate Sustainability ◽

Primary Source ◽

Text Analytics ◽

Useful Knowledge ◽

Word Cloud ◽

Textual Data ◽

Shareholder Resolution ◽

The Impact ◽

Insight Into

Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience.

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