Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran
{"title":"对医用大麻化学成分的机器学习揭示了超出安慰剂预期的镇痛作用。","authors":"Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran","doi":"10.1038/s43856-025-00996-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The efficacy of medical cannabis in alleviating pain has been demonstrated in clinical trials, yet questions remain regarding the extent to which specific chemical compounds contribute to analgesia versus expectation-based (placebo) responses. Effective blinding is notoriously difficult in cannabis trials, complicating the identification of compound-specific effects.</p><p><strong>Methods: </strong>In a prospective study of 329 chronic pain patients (40% females; aged 48.9 ± 15.5) prescribed medical cannabis, we examined whether the chemical composition of cannabis cultivars could predict treatment outcomes. We used a Random Forest classifier with nested cross-validation to assess the predictive value of demographics, clinical features, and approximately 200 chemical compounds. Model robustness was evaluated using six additional machine learning algorithms.</p><p><strong>Results: </strong>Here we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). This result is consistent across all models tested. While well-known cannabinoids such as THC and CBD provide limited predictive value, specific terpenoids, particularly α-Bisabolol and eucalyptol, emerge as key predictors of treatment response.</p><p><strong>Conclusions: </strong>Our findings demonstrate that pain relief can be predicted from cannabis chemical profiles that are unknown to patients, providing evidence for compound-specific therapeutic effects. These results highlight the importance of considering the full range of cannabis compounds when developing more precise and effective cannabis-based therapies for pain management.</p>","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":"5 1","pages":"295"},"PeriodicalIF":5.4000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267634/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations.\",\"authors\":\"Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran\",\"doi\":\"10.1038/s43856-025-00996-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The efficacy of medical cannabis in alleviating pain has been demonstrated in clinical trials, yet questions remain regarding the extent to which specific chemical compounds contribute to analgesia versus expectation-based (placebo) responses. Effective blinding is notoriously difficult in cannabis trials, complicating the identification of compound-specific effects.</p><p><strong>Methods: </strong>In a prospective study of 329 chronic pain patients (40% females; aged 48.9 ± 15.5) prescribed medical cannabis, we examined whether the chemical composition of cannabis cultivars could predict treatment outcomes. We used a Random Forest classifier with nested cross-validation to assess the predictive value of demographics, clinical features, and approximately 200 chemical compounds. Model robustness was evaluated using six additional machine learning algorithms.</p><p><strong>Results: </strong>Here we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). This result is consistent across all models tested. While well-known cannabinoids such as THC and CBD provide limited predictive value, specific terpenoids, particularly α-Bisabolol and eucalyptol, emerge as key predictors of treatment response.</p><p><strong>Conclusions: </strong>Our findings demonstrate that pain relief can be predicted from cannabis chemical profiles that are unknown to patients, providing evidence for compound-specific therapeutic effects. These results highlight the importance of considering the full range of cannabis compounds when developing more precise and effective cannabis-based therapies for pain management.</p>\",\"PeriodicalId\":72646,\"journal\":{\"name\":\"Communications medicine\",\"volume\":\"5 1\",\"pages\":\"295\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267634/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s43856-025-00996-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43856-025-00996-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations.
Background: The efficacy of medical cannabis in alleviating pain has been demonstrated in clinical trials, yet questions remain regarding the extent to which specific chemical compounds contribute to analgesia versus expectation-based (placebo) responses. Effective blinding is notoriously difficult in cannabis trials, complicating the identification of compound-specific effects.
Methods: In a prospective study of 329 chronic pain patients (40% females; aged 48.9 ± 15.5) prescribed medical cannabis, we examined whether the chemical composition of cannabis cultivars could predict treatment outcomes. We used a Random Forest classifier with nested cross-validation to assess the predictive value of demographics, clinical features, and approximately 200 chemical compounds. Model robustness was evaluated using six additional machine learning algorithms.
Results: Here we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). This result is consistent across all models tested. While well-known cannabinoids such as THC and CBD provide limited predictive value, specific terpenoids, particularly α-Bisabolol and eucalyptol, emerge as key predictors of treatment response.
Conclusions: Our findings demonstrate that pain relief can be predicted from cannabis chemical profiles that are unknown to patients, providing evidence for compound-specific therapeutic effects. These results highlight the importance of considering the full range of cannabis compounds when developing more precise and effective cannabis-based therapies for pain management.