Many governments in Europe have favoured more restrictive policies with respect to illicit drugs than for alcohol or tobacco, on the grounds that they regard both illicit drug abuse and related problems as a significantly larger problem for society58. Drug rankings can therefore be useful to inform policy makers and the public about the relative importance of licit drugs (including prescription drugs) and illicit drugs for various types of harm58.

Our MOE results confirm previous drug rankings based on other approaches. Specifically, the results confirm that the risk of cannabis may have been overestimated in the past. At least for the endpoint of mortality, the MOE for THC/cannabis in both individual and population-based assessments would be above safety thresholds (e.g. 100 for data based on animal experiments). In contrast, the risk of alcohol may have been commonly underestimated.

Our results confirm the early study of Gable6 who found that the margin of safety (defined as therapeutic index) varied dramatically between substances. In contrast, our approach is not based on a therapeutic index, which is not necessarily associated with risk, but uses the most recent guidelines for risk assessment of chemical substances, which also takes the population-based exposure into account.

A major finding of our study is the result that the risk of drugs varies extremely, so that a logarithmic scale is needed in data presentation of MOE (e.g. Figures 1–3). Therefore, we think that previous expert-based approaches which often applied a linear scale of 0–3 or 0–10039, might have led to a form of “egalitarianism”, in which the public health impact of drugs appears more similar than it is in reality (i.e. more than 10.000-fold different as shown in our results on a population basis, e.g. Fig. 2 and 3). As expected, for an individual the difference between the impact of different drugs is not as large as for the whole society (i.e. only up to 100 fold, Fig. 1).

According to the typical interpretation of MOEs derived from animal experiments, for individual exposure the four substances alcohol, nicotine, cocaine and heroin fall into the “high risk” category with MOE < 10, the rest of the compounds except THC fall into the “risk” category with MOE < 100. On a population scale, only alcohol would fall into the “high risk” category, and cigarette smoking would fall into the “risk” category. A difference between individual and whole population MOE was confirmed by the lack of correlation between average values (linear fit: R = 0.25, p = 0.53). This result is different to the previous expert-based surveys, for which the ranking performed at the population and individual level generally led to the same ranking (R = 0.98)3. Nevertheless, we judge our results as more plausible. For an individual heavy consumer of either heroin or alcohol, the risk of dying from a heroin overdose or from alcoholic cirrhosis increased considerably in each case. However for the society as a whole, the several ten-thousands of alcohol-related deaths considerably outnumber drug overdose deaths. Hence, it is plausible that the MOE for alcohol can be lower than the one for heroin, purely because of the high exposure to alcohol in the European society (see also Rehm et al.59).

Nevertheless, as previously stressed, our findings should not be interpreted that moderate alcohol consumption poses a higher risk to an individual and their close contacts than regular heroin use14. Much of the harm from drug use is not inherently related to consumption, but is heavily influenced by the environmental conditions of the drug use2, and this additional hazard is not included in a drug ranking based on (animal) toxicology.

The first major problem of the approach is the lack of toxicological dose-response data for all compounds except alcohol and tobacco. No human dose-response data are available; also no dose-response data in animals, only LD50 values are published. Furthermore, no chronic-toxicity data (long-term experiments) are available, which are usually used for such kinds of risk assessment. Therefore, we can assess only in regards to mortality but not carcinogenicity or other long-term effects. The absence of such data is specifically relevant for compounds with low acute toxicity (such as cannabis), the risk of which may therefore be underestimated.

Additionally, the available toxicological thresholds (i.e. LD50 values) have considerable uncertainty (for example, more than a factor of 10 for diazepam in different species). However it has been previously shown that the animal LD50 is closely related to fatal drug toxicity in humans60. The sensitivity analysis based on human data for ethanol shows that the average MOE result is similar to the result based on animal LD50. Our results for ethanol are also consistent with previous MOE studies of ethanol2021. For cannabis and nicotine, the discrepancy in the sensitivity analysis can be explained in the chosen endpoints (no dose response data on mortality in humans were identifiable in the literature). For example, the only available human toxicological endpoint for cannabis as chosen by EFSA55 was “psychotropic effects”. The rationale for choosing this endpoint was the exclusion of risk for the inadvertent and indirect ingestion of THC when hemp products are used as animal feed55. We were unable to identify dose-response information for other endpoints of cannabis (e.g. mental health problems, chronic risk, or other cannabis-constituents besides THC). We think that while it is clear that different endpoints may yield quite different results, the human MOE for cannabis based on the endpoint “psychotropic effects” can be seen as general validation of the MOE concept, because the resulting values below 1 are expected as the psychotropic effect is the desired endpoint (and hence the psychotropic threshold dose is exceeded by drug users). Similar to cannabis, the sensitivity analysis for nicotine based on human data resulted in much lower MOE values. This again is based on a different endpoint (increase of blood pressure in this case, which is expected to be more sensitive than mortality). We nevertheless think that the risks of cigarettes could have been underestimated in our modelling, because in contrast to the other agents, tobacco contains a multicomponent mixture of toxicants. Previous risk assessment of tobacco (both financed and co-authored by the tobacco industry) have looked at various compounds but not included nicotine itself2223. From the variety of investigated compounds in tobacco smoke, the lowest MOEs were found for hydrogen cyanide (MOE 15)22 and acrolein (MOE range 2–11)23. These values are reasonably consistent with our MOE for nicotine of 7.5 (individual exposure). However, it would be advisable for future risk assessments of tobacco smoking to include modelling of a combined MOE, which considers all toxic compounds.

The second major problem is the uncertainty in data about individual and population-wide exposure due to the illegal markets. There is a scarcity of epidemiological studies of cannabis use by comparison with epidemiological studies of alcohol and tobacco use61. If population data are available, they are usually provided as “% prevalence”, but for risk assessment we need a population-wide per-capita dosage in “mg compound/person/day”.

Due to both problems (or in other words the large uncertainty in input data of exposure), we cannot calculate with point estimates. To overcome this, we are using a probabilistic calculation methodology that takes the whole distribution of the input variables into account. For example, for the exposure a random sample of the number of days of annual drug use is combined with a random sample in the range of the usual dosages of the drug to provide an estimate for dosage.

The downside of the probabilistic approach is that the output also is not a single numerical value but rather a likelihood distribution. Nevertheless, using graphical approaches (Figs. 1–3) the results for all drugs under study can be quickly compared. On the other hand, this may be an advantage, as we did not try to establish a single value “to be written in stone”. The utility of “single figure index harm rankings” has also been questioned in general62.

Our approach contains some further limitations: Drug interactions cannot be taken into account as we just do not have any toxicological data on such effects (e.g. by co-administration in animals). However, polydrug use in humans is common, especially of illicit drugs with ethanol or benzodiazepines63. Addiction potential and risk of use (e.g. unclean syringes leading to increased infection risk) are also not considered by the model, because adequate dose-response data could not be identified for these endpoints.

Aside from the limitations in data, our results should be treated carefully particularly in regard to dissemination to lay people. For example, tabloids have reported that “alcohol is worse than hard drugs” following the publication of previous drug rankings. Such statements taken out of context may be misinterpreted, especially considering the differences of risks between individual and the whole population.

A main finding of our study is the qualitative validation of previous expert-based approaches on drug-ranking (e.g. Nutt et al.9), especially in regard to the positions of alcohol (highest) and cannabis (lowest). Currently, the MOE results must be treated as preliminary due to the high uncertainty in data. The analyses may be refined when better dose-response data and exposure estimates become available. As the problem is multidimensional15, it would also make sense to establish some form of harm or risk matrix64 that may be more suitable than a single indicator. Our MOE could be one piece in the puzzle that constitutes to the establishment of a “holistic drug risk”.

Currently, the MOE results point to risk management prioritization towards alcohol and tobacco rather than illicit drugs. The high MOE values of cannabis, which are in a low-risk range, suggest a strict legal regulatory approach rather than the current prohibition approach.