Haroon Taylor, Shazia Mahamdallie, Matthew Sawyer, Nazneen Rahman
{"title":"MCF 分类器:大规模估算、标准化和分层医药碳足迹","authors":"Haroon Taylor, Shazia Mahamdallie, Matthew Sawyer, Nazneen Rahman","doi":"10.1111/bcp.16229","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aims</h3>\n \n <p>Healthcare accounts for 5% of global greenhouse gas emissions, with medicines making a sizeable contribution. Product-level medicine emission data is limited, hindering mitigation efforts. To address this, we created Medicine Carbon Footprint (MCF) Classifier, to estimate, standardize, stratify and visualize medicine carbon footprints.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We used molecular weight and chemical structure to estimate the process mass intensity and global warming potential of the active pharmaceutical ingredient in small molecule medicines. This allowed us to estimate medicine carbon footprints per dose, which we categorized into MCF Ratings, accessible via a searchable web application, MCF Formulary. We performed comparison and sensitivity analyses to validate the ratings, and stratification analyses by therapeutic indication to identify priority areas for emission reduction interventions.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We generated standardized medicine carbon footprints for 2214 products, with 38% rated LOW, 35% MEDIUM, 25% HIGH and 2% VERY HIGH. These products represented 2.2 billion NHS England prescribed doses in January 2023, with a total footprint of 140 000 tonnes CO<sub>2</sub>e, equivalent to the monthly emissions of 940 000 cars. Notably, three antibiotics—amoxicillin, flucloxacillin and penicillin V—contributed 15% of emissions. We estimate that implementing the recommended 20% antibiotic prescription reduction could save 4200 tonnes CO<sub>2</sub>e per month, equivalent to removing 29 000 cars.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Standardized medicine carbon footprints have utility in assessing and addressing the carbon emissions of medicines, and the potential to inform and catalyse changes needed to align better healthcare and net zero commitments.</p>\n </section>\n </div>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bcp.16229","citationCount":"0","resultStr":"{\"title\":\"MCF classifier: Estimating, standardizing, and stratifying medicine carbon footprints, at scale\",\"authors\":\"Haroon Taylor, Shazia Mahamdallie, Matthew Sawyer, Nazneen Rahman\",\"doi\":\"10.1111/bcp.16229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aims</h3>\\n \\n <p>Healthcare accounts for 5% of global greenhouse gas emissions, with medicines making a sizeable contribution. Product-level medicine emission data is limited, hindering mitigation efforts. To address this, we created Medicine Carbon Footprint (MCF) Classifier, to estimate, standardize, stratify and visualize medicine carbon footprints.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We used molecular weight and chemical structure to estimate the process mass intensity and global warming potential of the active pharmaceutical ingredient in small molecule medicines. This allowed us to estimate medicine carbon footprints per dose, which we categorized into MCF Ratings, accessible via a searchable web application, MCF Formulary. We performed comparison and sensitivity analyses to validate the ratings, and stratification analyses by therapeutic indication to identify priority areas for emission reduction interventions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We generated standardized medicine carbon footprints for 2214 products, with 38% rated LOW, 35% MEDIUM, 25% HIGH and 2% VERY HIGH. These products represented 2.2 billion NHS England prescribed doses in January 2023, with a total footprint of 140 000 tonnes CO<sub>2</sub>e, equivalent to the monthly emissions of 940 000 cars. Notably, three antibiotics—amoxicillin, flucloxacillin and penicillin V—contributed 15% of emissions. We estimate that implementing the recommended 20% antibiotic prescription reduction could save 4200 tonnes CO<sub>2</sub>e per month, equivalent to removing 29 000 cars.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Standardized medicine carbon footprints have utility in assessing and addressing the carbon emissions of medicines, and the potential to inform and catalyse changes needed to align better healthcare and net zero commitments.</p>\\n </section>\\n </div>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bcp.16229\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/bcp.16229\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/bcp.16229","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
MCF classifier: Estimating, standardizing, and stratifying medicine carbon footprints, at scale
Aims
Healthcare accounts for 5% of global greenhouse gas emissions, with medicines making a sizeable contribution. Product-level medicine emission data is limited, hindering mitigation efforts. To address this, we created Medicine Carbon Footprint (MCF) Classifier, to estimate, standardize, stratify and visualize medicine carbon footprints.
Methods
We used molecular weight and chemical structure to estimate the process mass intensity and global warming potential of the active pharmaceutical ingredient in small molecule medicines. This allowed us to estimate medicine carbon footprints per dose, which we categorized into MCF Ratings, accessible via a searchable web application, MCF Formulary. We performed comparison and sensitivity analyses to validate the ratings, and stratification analyses by therapeutic indication to identify priority areas for emission reduction interventions.
Results
We generated standardized medicine carbon footprints for 2214 products, with 38% rated LOW, 35% MEDIUM, 25% HIGH and 2% VERY HIGH. These products represented 2.2 billion NHS England prescribed doses in January 2023, with a total footprint of 140 000 tonnes CO2e, equivalent to the monthly emissions of 940 000 cars. Notably, three antibiotics—amoxicillin, flucloxacillin and penicillin V—contributed 15% of emissions. We estimate that implementing the recommended 20% antibiotic prescription reduction could save 4200 tonnes CO2e per month, equivalent to removing 29 000 cars.
Conclusions
Standardized medicine carbon footprints have utility in assessing and addressing the carbon emissions of medicines, and the potential to inform and catalyse changes needed to align better healthcare and net zero commitments.