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Background <p>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> Methods <p>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> Results <p>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; <i>p</i> &lt; 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> Conclusions <p>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>

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Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月16日

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  • Adi Hatav,
  • Yelena Vysotski,
  • Anna Shapira,
  • Shiri Procaccia,
  • David Meiri,
  • Dvir Aran

摘要

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.