The use patterns of novel psychedelics: experiential fingerprints of substituted phenethylamines, tryptamines and lysergamides

Abstract

Background
Novel psychedelics (NPs) are an expanding set of compounds, presenting new challenges for drug policy and opportunities for clinical research. Unlike their classical derivatives, little is known regarding their use profiles or their subjective effects.

Aims
The purpose of this study was to compile usage patterns and adverse event rates for individual NPs belonging to each of three main psychedelic structural families. Targeting the most widely used representatives for each class, we expanded on their phenomenological distinctions.

Methods
A two-part survey was employed. We investigated the prevalence of novel phenethylamines, tryptamine and lysergamides in NP users ( N = 1180), contrasting the type and incidence of adverse events (AEs) using a set of logistic regressions. Honing in on 2–4-Bromo-2,5-dimethoxyphenyl)ethanamine (2C-B) (48.6%), 1-propionyl-lysergic acid diethylamide (1P-LSD) (34.2%) and 4-Acetoxy-N,N-dimethyltryptamine (4-AcO-DMT) (23.1%), we examined their phenomenological separability using a gradient boosting (XGBoost) supervised classifier.

Results
Novel phenethylamines had the highest prevalence of use (61.5%) seconded by tryptamines (43.8%) and lysergamides (42.9%). Usage patterns were identified for 32 different compounds, demonstrating variable dosages, durations and a common oral route of administration. Compared to phenethylamines, the odds for tryptamines and lysergamides users were significantly less for overall physical AEs. No significant differences in overall psychological AEs were found. Overall model area under the curve (AUC) stood at 0.79 with sensitivity (50.0%) and specificity (60.0%) for 2C-B ranking lowest.

Conclusion
NP classes may hold distinct AE rates and phenomenology, the latter potentially clouded by the subjective nature of these experiences. Further targeted research is warranted.

Full text

Vol.:(0123456789)
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https://doi.org/10.1007/s00213-022-06142-4
ORIGINAL INVESTIGATION
The use patterns ofnovel psychedelics: experiential fingerprints
ofsubstituted phenethylamines, tryptamines andlysergamides
P.Mallaroni1 · N.L.Mason1· F.R.J.Vinckenbosch1· J.G.Ramaekers1
Received: 29 September 2021 / Accepted: 6 April 2022
© The Author(s) 2022
Abstract
Background Novel psychedelics (NPs) are an expanding set of compounds, presenting new challenges for drug policy and
opportunities for clinical research. Unlike their classical derivatives, little is known regarding their use profiles or their
subjective effects.
Aims The purpose of this study was to compile usage patterns and adverse event rates for individual NPs belonging to each
of three main psychedelic structural families. Targeting the most widely used representatives for each class, we expanded
on their phenomenological distinctions.
Methods A two-part survey was employed. We investigated the prevalence of novel phenethylamines, tryptamine and lyser-
gamides in NP users (N = 1180), contrasting the type and incidence of adverse events (AEs) using a set of logistic regres-
sions. Honing in on 2–4-Bromo-2,5-dimethoxyphenyl)ethanamine (2C-B) (48.6%), 1-propionyl-lysergic acid diethylamide
(1P-LSD) (34.2%) and 4-Acetoxy-N,N-dimethyltryptamine (4-AcO-DMT) (23.1%), we examined their phenomenological
separability using a gradient boosting (XGBoost) supervised classifier.
Results Novel phenethylamines had the highest prevalence of use (61.5%) seconded by tryptamines (43.8%) and lyserga-
mides (42.9%). Usage patterns were identified for 32 different compounds, demonstrating variable dosages, durations and
a common oral route of administration. Compared to phenethylamines, the odds for tryptamines and lysergamides users
were significantly less for overall physical AEs. No significant differences in overall psychological AEs were found. Overall
model area under the curve (AUC) stood at 0.79 with sensitivity (50.0%) and specificity (60.0%) for 2C-B ranking lowest.
Conclusion NP classes may hold distinct AE rates and phenomenology, the latter potentially clouded by the subjective nature
of these experiences. Further targeted research is warranted.
Keywords Psychedelic· Tryptamine· Lysergamide· Phenylethylamine· Novel psychoactive substance· Hallucinogen·
2–4-Bromo-2,5-dimethoxyphenyl)ethanamine (2C-B)· 4-Acetoxy-N,N-dimethyltryptamine (4-AcO-DMT)· 1-propionyl-
lysergic acid diethylamide (1P-LSD)
Introduction
The subjective qualities of a drug often mould its notoriety.
Such attributes are notably associated with the psychedelic
class of psychoactive substances. Described as ‘mind-
manifesting’ substances, psychedelics are characterised by
profound distortions in sensory perception and subjective
experience of one’s self, as well as alterations in mood, cog-
nition and thought (Nichols 2016; Preller and Vollenwei-
der 2016). A growing body of evidence has indicated that
classic psychedelics such as psilocybin, dimethyltryptamine
(DMT), lysergic acid diethylamide (LSD) and mescaline are
safe and may be of clinical use for a range of psychiatric
disorders (Chi and Gold 2020), bringing forth significant
This article belongs to a Special Issue on Psychopharmacology on
Psychedelic Drugs.
* P. Mallaroni
p.mallaroni@maastrichtuniversity.nl
* J. G. Ramaekers
j.ramaekers@maastrichtuniversity.nl
1 Department ofNeuropsychology andPsychopharmacology,
Faculty ofPsychology andNeuroscience, Maastricht
University, P.O. Box616, 6200, MD, Maastricht,
theNetherlands
/ Published online: 30 April 2022
Psychopharmacology (2022) 239:1783–1796
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1 3
clinical and public interest, with their rising use (Yockey
etal. 2020; Yockey and King 2021) hotly discussed by a
diaspora of media (Aday etal. 2019). That said, classic
psychedelics are currently scheduled as drugs of abuse under
most national drug policies (Belouin and Henningfield 2018)
and are thus illegal to purchase or manufacture. This discrep-
ancy between growing public interest and lack of availability
may arguably be fuelling a marketplace for accessible psy-
chedelic counterparts, which circumvent existing legislation.
Novel psychedelics (NPs) are defined by regulatory
authorities as novel psychoactive substances: drugs not
controlled by the 1961 United Nations Single Convention
on Narcotic Drugs or the 1971 United Nations Convention
on Psychotropic Substances, yet are liable to abuse and/or
dependence, producing similar effects to scheduled com-
pounds (Schifano etal. 2015). NPs are typically synthetic
pharmacophores of classical psychedelics, with their entry
into the recreational market traced back to the pharmacopea
of compounds published by Alexander Shulgin, detailing
the effects and synthesis routes of over 200 novel hallucino-
gens, deliriants and stimulants (Shulgin and Shulgin 1991,
1997). Consequently, NPs are often more accessible than
their Schedule I counterparts, sold online under a number
of aliases (Schmidt etal. 2011) (Schmidt etal. 2011; Smith
etal. 2015; Miliano etal. 2018) and gaining traction among
recreational users (Neicun etal. 2020). However, they are
distinct from their progenitors in that they lack a long his-
tory of human use and substantial research data. While prior
work has begun to apply risk-classifications on the basis of
individual clinical reports (Bersani etal. 2014; Corkery etal.
2020; Nugteren–van Lonkhuyzen 2020), and investigated
both the possibility of enhanced likelihood of reduced men-
tal wellbeing or potential therapeutic benefits pertaining to
their use, a fine-grain understanding of individual NP use
patterns has yet to be compiled. Quantitative descriptions of
dose, route of administration, duration of effects and experi-
ence of (sub)acute psychological and physiological risks are
the first key steps in informing harm reduction approaches.
Currently legislative strategies regarding NP regulation
consist of a ‘cat and mouse chase’ in which attempts to
restrict the use of a particular NP are met with the appear-
ance of several-fold more, spreading toxicological evalu-
ations thin. In this regard, consideration and identification
of the family of chemical compounds that an NP falls in
may be informative. Namely, novel (and classical) psych-
edelics are typically segregated into three structural fami-
lies: tryptamines such as 4-AcO-DMT, lysergamides such
as LSD and the NP 1P-LSD or phenethylamines such as
mescaline and 2C-B (Nichols 2012). While the primary
mechanism of action for their hallucinogenic effects in
humans is attributed to serotonin 5-HT2A agonism (Kom-
eter etal. 2013; Preller etal. 2018; Nutt etal. 2020), accu-
mulating evidence also emphasises the role of differential
binding profile action at secondary sites such as seroto-
nin 5HT2C and 5HT1A receptors, dopaminergic receptors
and involvement of the glutamatergic system (Ray 2010;
Studerus etal. 2012; Mason etal. 2020; Vollenweider and
Preller 2020). As the families are differentiated via struc-
ture, they are proposed to have different binding affinities
at both primary (5-HT2A) and secondary sites, resulting
in differing in levels of potency, effect duration and likely
subjective effect profiles (Leth-Petersen etal. 2014; Hal-
berstadt etal. 2020), the latter of which is closely tied to
outcomes in classical counterparts. Namely, experiential
facets such loss of oneself and sentiments of unity and
harmony have repeatedly been shown to drive positive psy-
chological markers in studies employing healthy and clini-
cal populaces (Roseman etal. 2018; Yaden and Griffiths
2020). Concerning this aspect, data-driven approaches
using machine learning (ML) have proven themselves to be
particularly sensitive in demonstrating structural-experien-
tial alignment of psychedelics regarding the semantic con-
tent of these experiences (Zamberlan etal. 2018; Martial
etal. 2019). By operating in an agnostic manner to capture
non-linear multi-dimensional interactions and infer the
degree of class ownership, these tools may be better suited
to explain the distinctions between structural classes than
hard, binary decision-boundaries set by a-priori assump-
tions in classical hypothesis-testing approaches (Rutledge
etal. 2019; Li and Tong 2020). Decision-tree ML mod-
els are particularly favourable when seeking to explain
variables of interests from non-normally distributed data
such as self-reported independent subjective experiences
and derive good explanatory value even in the presence of
major scoring noise (Shanthini etal. 2019). Together with
their redeployable nature once trained, they be may useful
tools to generalise measures of subjective effects.
Thus, the question arises if the different psychedelic fami-
lies also have different risk and benefit profiles. Quantita-
tively comparing the propensity of adverse side effects, as
well as the subjective effect profile of the different families
of NPs, may elucidate important factors to consider regard-
ing identifying concerns of emerging NPs. Paired with
information regarding current use practices, such findings
would serve as a first step to focus future studies onto spe-
cific NP families, ultimately helping derive which classes
may be most relevant for clinical study. The aims of the
present survey study were therefore twofold. First, we aimed
to establish current patterns of NP use as to assess whether
the propensity for adverse effects differentiated between NP
classes. Second, taking into consideration the importance
of the experiential aspect of psychedelics, we explored the
phenomenological separability of each class using a set of
representatives for tryptamines (4-AcO-DMT), lyserga-
mides (1P-LSD) and phenethylamines (2C-B). By using an
extreme gradient boosting XGBoost algorithm, we highlight
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1 3
the suitability of exploratory ML approaches for the study
of subjective drug effects.
Methods
Design
The study employed an unincentivised, anonymous online
survey, promoted as an investigation into the use and effects
of novel psychedelic substances. Advertisements were
placed on Internet fora related to psychedelic drug use,
such as psychonautwiki.org and Open Foundation. The
survey was regularly disseminated on discussion boards
pertaining to NPS use including Bluelight.com, Reddit (R/
ResearchChems, R/Psychedelics/ etc.) and Drugsforum.nl.
The eligibility criteria for participation consisted of being
18years or older, having experience with a novel psyche-
delic substance and providing informed consent. Ethical
approval was obtained from the Ethics Review Committee
of Psychology and Neuroscience of Maastricht University
(ERCPN- 222_77_04_2020).
The survey was created and hosted on the Qualtrics soft-
ware platform (XM 12). As to gather information on gen-
eral use frequencies and information pertaining to qualita-
tive components of NPS effects, the survey was subdivided
into two sections: a first half pertaining to general use and
a second revolving around a recent (<
6months) full-dose
experience with a novel psychedelic.
Between May 2020 and January 2021, 2700 responses
were collected of which 1180 respondents were 18years or
older, provided informed consent and completed the first
half of the questionnaire. Of these, a subset of 599 respond-
ents provided information pertaining to a recent experience
with an NPS. The duration of the survey was dependent on
the number of drugs a respondent chose to provide informa-
tion on as well as their choice to continue with questions
pertaining to a recent experience. It was possible to pause
the survey and complete it at another time. The average sur-
vey completion time was 15minutes.
Measures
Demographics
Background information collected consisted of age, biologi-
cal sex, highest education level achieved and continent of
residence. Classical psychedelic use history was assessed
by providing a selection of substance choices: psilocybin,
MDMA, ayahuasca, DMT, LSD, mescaline and the alterna-
tive option of no prior experience.
General NPS use
Participants were first asked about their previous experience
with each of the three structural families of psychedelics,
followed by the option to provide information particular to
a listed example. Each choice was precluded with examples
of representatives for each class. Drug selection was centred
around previously documented, recreationally used novel
psychedelics of which 16 phenethylamines, 13 tryptamines
and 4 lysergamides were selected (for a complete list all
compounds, see Table1). Choices for each structural family
were supplemented by an ‘other’ text option to provide the
opportunity to include an unlisted substance.
Binary (yes/no) questions were employed to evaluate the
occurrence of clinically relevant psychological and/or physi-
cal adverse events (AEs), each of which was supplemented
by additional subcategories (Physical: Gastrointestinal,
Cardiovascular, Seizures; Psychological: Anxiety, Paranoia,
Low mood). The choice of these subtypes was defined by
prior literature on serotonergic classical and novel psych-
edelics (Nichols 2016; Dos Santos etal. 2018; Luethi and
Liechti 2020). As a follow-up, we asked users whether these
effects overall occurred acutely or long term (after the dis-
sipation of drug effects).
Recent NP experience
Upon completion of the first half of the survey, respond-
ents were offered the possibility of providing information
on a particular psychedelic experience they had in the last
6months using an NP. It was stressed that their choice
should consist of a “full” experience (one of noticeable per-
ceptual effects). Their choice was facilitated by providing a
list of all previously suggested NP representatives, alongside
an “other” category. Due to the expected popularity of 2C-B
and 4-AcO-DMT and with the large number of choices made
available in the survey, an option was provided to encourag-
ing users to detail one or the other. Following their selection
of a compound, respondents were once again prompted for
the estimated dose of this full experience.
As to identify the experiential components that define
the phenomenology of a particular NP experience, partici-
pants were subject to standardised questionnaires assess-
ing drug effects retrospectively. Employed in clinical trials
evaluating the acute effects of psychoactive drugs, these
also serve the dual purpose of establishing qualitative ref-
erence points for data on yet-trialled NPS. The 5D-ASC
scale measures altered states of consciousness and con-
tains 94 items in the form of visual analogue scales. The
instrument consists of five dimensions comprising Oce-
anic Boundlessness, Anxious Ego Dissolution, Vision-
ary Restructuralisation, Vigilance Reducton, Auditory
Alterations and 11 subscales (Studerus etal. 2010). The
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1 3
nature of these subscales is described in the supplemen-
tary materials. The 5D-ASC has been validated using a
range of hallucinogens, entactogens, stimulants and non-
pharmacological altered states of consciousness (Liechti
etal. 2017; Mueller etal. 2018; Kuypers etal. 2019; Luke
etal. 2019; Holze etal. 2020; Mason etal. 2020; Uthaug
etal. 2021).
Efforts are underway to produce a compendium of drug
phenomenology (Schmidt and Berkemeyer 2018) as to
establish points of reference. To extend the generalisabil-
ity of potential findings, respondents were also asked to
complete in supplement the 48-item Addiction Research
Centre Inventory (ARCI) (Martin etal. 1971), previously
employed in studies of NPs and other psychoactives (Papa-
seit etal. 2018; Papaseit etal. 2020). Going further, as
subjective experiences under psychedelics are coloured
by extraneous contextual factors such as set and setting
(Hartogsohn 2016). We provide in addition as control
variables, use motivation as assessed in prior evaluations
of NPs endorsed motives (Kettner etal. 2019) alongside
details regarding the environment in which this recent
experience took place. Methods and results pertaining
to these inventories can be found in the supplementary
materials.
Statistics
General
Survey data were cleaned using SPSS Version 24.0.
Respondents who failed to complete the first half of the sur-
vey were excluded.
Follow-up questions on a chosen substance were retained
based on dose validity. As mean recreational doses are
likely subject to significant variance due to intraindividual
motives, tolerance and lack of exact dose knowledge, we
characterised outliers as incorrectly used mass (mg/g/μg)
metrics. Shapiro-Wilks tests were conducted prior to analy-
ses to examine the homogeneity of variance for all continu-
ous variables. Incorrectly used metric outliers were defined
by (1) visual identification (ex: 1000g) and (2) a Box-Cox
power transformation followed by Z-score rescaling. Points
found to be
≥
than 3 S.D were excluded. Proportions (%)
are reported for sex, gender, education, continent of origin,
education-level and classical psychedelic history. Mean
(± SD) is given for age. Frequencies and proportions pre-
sented for individual drug use results are weighed accord-
ing to the total respective family sample size (phenethyl-
amines, tryptamines, lysergamides). In the case of follow-up
Table 1 Listed survey NPS choices. Each choice made available to
users to select from are organised according to their structural sub-
specifications.†While 5-MeO-DMT is a natural indolealkylamine
extracted from Bufo alvarius toad venom (Weil and Davis 1994),
prior investigations have classified it as an NPS (Khaled etal. 2016)
Phenethylamines Tryptamines Lysergamides
N-(2-methoxybenzyl) phenethylamines (25X-NBOMes) N, N-diisopropyltryptamines LSD analogues
 25b-NBOMe 4-HO-DiPT 1P-LSD
 25c-NBOMe 4-AcO-DiPT ALD-52
 25i-NBOMe DiPT AL-LAD
DPT LSA
Substituted dimethoxyphenethylamines
(2C-Xs) N-methyl-N-isopropyltryptamines
 2C-B MiPT
 2C-C 5-MeO-MiPT
 2C-D 4-HO-MiPT
 2C-E
 2C-I N, N-diallyltryptamines
 2C-P 5-MeO-DALT
 2C-T-2
 TCB2 Psilocin derivatives and homologues
 Bromo-DragonFly 4-AcO-DMT
4-AcO-MET
4-Substituted-2,5-dimethoxyamphetamines (DOx) 4-HO-DET
 DOB 4-HO-MET
 DOC
 DOI N, N-dimethyltryptamine derivatives
 DOM 5-MeO-DMT†
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1 3
questions, proportions are listed in relation to the individual
drug sub-population. In the case of dose, results are reported
as the median dose alongside the interquartile range (IQR).
For all tests, statistical significance was defined by p < 0.05.
Class anddrug comparisons
A set of logistic regression analyses were employed to inves-
tigate associations of our dependent variables of interest:
(1) incidence of physical side effects; (2) incidence of psy-
chological side effects; (3) type of adverse effect (Physical:
gastrointestinal, cardiovascular, seizures; Psychological:
anxiety, paranoia, low mood); and (4) the duration of the
side effect (acute/long-term).
Due to the nature of the study, no suitable drug-naïve
reference group was available. As such, in all regression
models, phenethylamines were set as the intercept (β0),
having the largest number of observations to ensure suf-
ficient statistical power. For each model, we included age
and biological sex as confounders. Consequently, adjusted
odds ratios (aORs) are reported therein. Being aware that
the orthogonality of our predictors may be affected by the
nested, multi-choice format of our survey, we calculated
each of their variance inflation factors (VIF) (Midi etal.
2010). When contrasted to phenethylamine use, neither
tryptamine use (1.24) nor lysergamide use (1.23) produced
scores beyond a conservative 2.5 VIF threshold, reflecting
low collinearity.
Supervised classification ofsubjective drug effects
The classification algorithm was trained and validated
using the most popular compound of each structural family,
defined by the largest number of observations. Our choice
of canonical class members was narrowed down to 2C-B
(phenethylamines, n = 176), 4-AcO-DMT (tryptamines,
n =
59) and 1P-LSD (lysergamides, n =
102). Due to their
diminished dimensionality, we trained our model on the five
core facets of the 5D-ASC. Model development and evalu-
ation were conducted using the following Python toolkits:
Imbalanced-learn and Scikit-learn (Pedregosa etal. 2011).
We selected for Extreme Gradient Boosting (XGboost), a
decision tree-based machine learning method (Chen and
Guestrin 2016) to build the classifier algorithm. This was
based on its robustness to feature multicollinearity, inherent
feature selection, capacity to handle sparse data and detect
non-linear relationships between variables. Furthermore,
XGBoost’s inherent design allows for high interpretabil-
ity: by employing a recursive tree-based decision system in
which several weak trees are combined in order to generate a
collectively strong model, the importance of each individual
feature used is determined by its accumulated use in each
decision step in trees. This computes a metric characterising
the relative importance of each feature for each learning
step, otherwise absent in other ML approaches. This feature
importance is valuable for estimating features that are the
most discriminative of model outcomes, especially when
they are related to meaningful clinical parameters.
Datasets with low numbers of observations and numerous
dependable variables are often subject to overfitting (Ying
2019). We therefore took pre-processing steps to normalise
and resample features as to improve model generalisability
prior to training (these details can be found in the supple-
mentary materials). Controlling model bias-variance trade-
off is a key task in machine learning (Cawley and Talbot
2010). One optimal approach to this is nested cross-valida-
tion (CV), an equivalent to creating multiple train-test splits
to derive robust estimates of model predictive performance
in unseen data (Varma and Simon 2006; Krstajic etal. 2014).
Following pre-processing, we used a tenfold nested K-fold
CV (Scikit-learn), wherein at each iteration, 5 of the folds
were used in the inner loop to tune model parameters and
train the algorithm, and the 5th fold was used in the outer
loop to test the trained model. Tuned model parameters
included the number of trees (100 to 1000), tree depth (1, 2
or 3 to allow for higher order interactions) and the learning
rate (0.1 to 0.3). Training of the XGBoost model was based
on this tenfold stratified CV repeated 3 times, using the aver-
age AUC of all possible pairwise combinations of classes
(Hand and Till 2001). The area under the receiver operating
characteristic curve (AUROC), or AUC, was calculated for
each class. AURC provides an aggregate measure of per-
formance across all possible classification thresholds, by
contextualising sensitivity (sensitivity) as a function of the
non-specificity (1 — specificity) for a classifier as classifica-
tion thresholds are varied. To aid in interpretation, Cohen’s
d equivalents of AUC scores list an AUC
=
0.58 as a small
effect size (0.2), AUC = 0.69 a medium effect size (0.5) and
AUC
= 0.79 a large effect size (0.8) (Salgado 2018). For
completeness, we report model feature importance, class-
specific and macro-average F1 scores, precision and recall.
Definitions for each additional measure and a description of
the model are found in the supplementary materials.
While ML multivariate approaches offer the opportunity
to derive latent patterns on yet-seen data, they perform blind
to the underlying distribution, working on approximations
derived from training data. As such, ML models may form
assumptions about a population which may not be represent-
ative of the true sample distribution (Li and Tong 2020). To
cross-examine whether flagged model distinctions stemmed
from random phenomena, we performed nonparametric, uni-
variate Kruskal–Wallis one-way analysis of variance tests
to confirm group differences. For completeness, post hoc
multiple comparisons were performed using Bonferroni-
corrected (p < 0.017) pairwise Dunn’s tests, described in
the supplementary materials.
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1 3
Results
The final sample of 1180 respondents consisted of 994 males
(84.2%) and 186 females (15.8%) with a mean age of 26.4
(SD: 8.4 range 18–64). Most of the sample had reached a
tertiary level education at a university/trade school/college
(68.4%), seconded by a high school diploma/equivalent
(29.9%) and followed by primary/elementary education
(1.7%). Participants were based in North America (50.7%),
Europe (45.3%) and Oceania (2.5%) with a minority from
South America (0.8%), Asia (0.5%) and Africa (0.2%). The
vast majority of respondents (96.8%) had previously used a
classical psychedelic, including psilocybin (80.8%), MDMA
(76.9), DMT (40.7%), mescaline (19.10%) and ayahuasca
(8.3%). Respondents were poly-users, with 85.7% of the
sample having tried more than one classical psychedelic, an
average of 3.1 (SD: 1.4).
General NPS use
Frequency ofuse
Of the three main families of NPS, phenethylamines had the
highest prevalence of use (61.5%), seconded by tryptamines
(43.8%) and lysergamides (42.9%). While a variety of drugs
were reported to have been tried, 2C-B was the most used
NP (48.6%), followed by 1P-LSD (34.2%) and 4-AcO-DMT
(23.1%). Raw percentages and frequencies for prior NPS use
can be found in Fig.1a. Users had experience with a range of
novel psychedelic drugs, trying an average of 5.9 (SD: 4.0)
of the 33 available compounds.
For each of the classes, several respondents chose to
provide an alternative substance, accounting for 19.3%,
10.3% and 27.7% of phenethylamines, tryptamines and
lysergamides respectively. Recurring compounds included
the phenethylamines 2C-B-FLY (26.4%) and 25e-NBOH
(6.4%), the tryptamines MET (13.2%) and 4-AcO-DET
(11.3%), and the lysergamides 1cP-LSD (45.7%) and ETH-
LAD (34.2%). Written-in responses were excluded from the
ensuing report due to their large heterogeneity in the number
of compounds listed at one time. TCB-2 was not included in
subsequent reporting due to a lack of observations (0.1%).
Patterns ofuse
Users reported a large range of doses for each compound.
Due to the skewed nature of the data, median doses (mg) and
their interquartile range (IQR) across all modes of adminis-
tration are reported in Fig.2a.
The substituted phenethylamine class of NBOMe’s such
as 25i-NBOMe (median: 750.0μg, IQR: 400.0) and lyser-
gamides such as 1P-LSD (median: 150.0μg, IQR: 100)
had the highest overall self-reported potency, as indicated
by the notable microgram range of doses. Conversely, for
each of their respective classes, the 2C-X compounds such
as 2C-D and the lysergamide AL-LAD were the upper
scales of identified doses. Most notably, tryptamines pre-
sented the highest recreational listed doses, with DPT
users reporting the largest used dose (median 50mg, IQR:
Fig. 1 Self-reported NPS use and adverse effects per structural fam-
ily. a Percentage of NPs reported to have been previously tried by
respondents. In (b) can be seen the incidence rate of adverse physical
and psychological side effect for each drug. For both (a) and (b), pro-
portions are listed in relation to each colour-matched family sample
size
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1 3
40). Data pertaining to the mean reported doses per route
of administration are found in the TableS1.
Similarly, effect duration was stratified across drug
families. For each drug, durations are represented as
frequency density estimates in Fig.2b alongside their
IQR. Median effect duration for phenethylamines was
found to be 6h whereas for lysergamides, it was 10h and
tryptamines 4h. These durations were further differenti-
ated according to the nature of the drug, with users of
the DO-X compound DOI reporting the longest lasting
effects with a median effect duration of more than 24h
whereas the shortest being for the tryptamine 5-MEO-
DMT at < 30min. Novel psychedelics were reported to
have been taken in a variety of ways. Oral intake was the
most frequently reported mode of administration across all
phenethylamines (69.7%), tryptamine (65.8%) and lyser-
gamide users (56.7%). Noteworthy contenders included
sublingual intake for phenethylamines (13.7%) and lyser-
gamides (43.3%), whereas for tryptamines, inhalation
comprised the second most popular option (15.3%). Where
available, median doses and durations separated by each
mode of administration are reported in the supplementary
materials TablesS1 and S2.
Side effects
For each drug, users were asked if they had previously expe-
rienced any overall physical or psychological side effects
(Fig.1b).
Physical side effects Binary logistic regression analyses
(Table2) revealed that in contrast to phenethylamines, lyser-
gamides (aOR = 0.53; p < 0.001, 95% CI [0.43–0.66]) and
tryptamines (aOR = 0.38; p <
0.001, 95% CI [0.31–0.47])
users reported significantly less overall physical AEs.
At a compound level, for phenethylamines, physical AEs
were most frequently reported by Bromo-Dragonfly users
(61.5%), 25i-NBOMe (60%) and DOB (58.8%) users. As
for lysergamides, these were most frequently reported by
1P-LSD (38.3%), LSZ (23.5%) and AL-LAD (19.1%) users,
whereas MiPT (60%), 5-MeO-MiPT (49.2%) and 5-MeO-
DiPT users (44.4%) represented the highest incidence rates
for tryptamines. In comparison to phenethylamines, risk of
gastrointestinal and cardiovascular side effects were sig-
nificantly lower for tryptamines ((aOR
=
0.48; p < 0.001,
95% CI [0.38–0.59]) and (aOR
=
0.42 (p < 0.001, 95% CI
[0.32–0.59])). Tryptamines were significantly less likely
Fig. 2 Patterns of NPS use relating to dose, duration and mode of
administration. a depicts median dosage (mg) and the IQR for each
drug across all modes of administration, and (b) shows a ridge plot
of mean drug effect duration across all modes of administration.
Filled lines represent the median, and dotted lines reflect the IQR. A
smoothing kernel of 0.7 was applied for this visualisation. (c) Admin-
istration routes in proportion of individual NP use
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1 3
to produce seizure-type AE; aOR 0.23 (p < 0.05, 95% CI
[0.08–0.59]; with lysergamides having the lowest reported
odds of aOR = 0.04 (p < 0.001, 95% CI [0.01–0.14]). Over-
all, the acute incidence of physical side effects associated
to tryptamine use was significantly less likely than that for
phenethylamines (aOR = 0.43 (p < 0.001, 95% CI [0.32–
0.59])) Due to extreme heteroscedasticity, maximum likeli-
hood estimation for models pertaining to extended AE dura-
tion (long term/both) is not reported.
Psychological side effects Odds ratios for phenethylamines,
tryptamines and lysergamides did not significantly differ
between groups in the case of overall psychological AEs
(Table2).
However, compounds expressed heterogeneous inci-
dence rates. Once more, the phenethylamines Bromo-
Dragonfly (61.5%), 25i-NBOMe (57.4%) and DOB
(54.7%) had the highest rate of psychological AEs.
Of all, the tryptamines 4-AcO-DiPT (92.9%), 4-HO-
DiPT (80.4%) and 5-MeO-DiPT (74%) ranked high-
est. No individual lysergamide held an incidence rate
over 50%, the highest scorers being 1P-LSD (38.3%),
LSZ (23.5%) and AL-LAD (19.1%). Psychologi-
cal effects did not significantly differ among classes
(p >
0.1). However, the incidence of specific psycho-
logical AEs varied between compounds. Lyserga-
mides were significantly more likely to produce anxi-
ety (aOR = 1.49 (p <
0.001, 95% CI [1.18–1.88]) and
paranoia (aOR = 1.62 (p <
0.01, 95% CI [1.20–2.20])
whereas both lysergamides (aOR
=
0.33 (p < 0.001,
95% CI [0.22–0.48])) and tryptamines (aOR = 0.63
(p < 0.05, 95% CI [0.42–0.94])) showed significantly
lesser odds of low mood than phenethylamines. Both
acute and long-term psychological adverse events were
significantly less likely for tryptamines in compari-
son to phenethylamines (aOR = 0.35 (p < 0.01, 95% CI
[0.16–0.75])).
Retrospective reports ofnovel psychedelic
experiences
Of the total sample, 599 respondents (50.8%) chose to
continue and provided details regarding a recent, full-
dose, psychedelic experience with an NPS. Reflecting our
previous general use findings, 2C-B, 1P-LSD and 4-AcO-
DMT were the most frequently chosen options, at 29.4%
(N = 176), 17% (N = 102) and 9.8% (N = 59) of the final
sample respectively. The full scope of NPs listed by users
can be found in TableS4.
Subjective effects of2C‑B, 4‑AcO‑DMT and1P‑LSD
The acute effects of a recent, full-dose experience with
2C-B, 4-AcO-DMT and 1P-LSD were retrospectively
assessed using the 5D-ASC (Fig.3a). Violin plots for all
scales including the 11D-ASC and ARCI are provided in
Fig. S4(a). Individual multiple regressions for each com-
pound did not identify the presence of dose–response rela-
tionships for any of the scales (p > 0.1). Confirmator y Wil-
coxon-signed rank testing (see supplementary materials)
demonstrated significant main effects for each of the 5 main
dimensions (χ2(2) = 10.9p = < 0.05–χ2(2) = 40.9p = < 0.0
01), reflecting non-spurious differences between groups.
Table 2 Odds ratios for
tryptamines and lysergamide
adverse event incidence, type
and duration in comparison
to phenethylamines. Adjusted
odds ratios (aORs) for each
dependent variable are listed
with confidence intervals
([CI] *p < 0.05, **p < 0.001,
***p < 0.001. p values for
intercept significance are
listed in the same manner. Cell
counts < 30 are left blank (.) due
to extreme heteroscedasticity
Phenethylamines
β0intercept pTryptamines aOR [CI] Lysergamides aOR [CI]
Physical adverse effects *** 0.38 [0.31–0.47]*** 0.53 [0.43–0.66]***
Gastrointestinal *** 0.48 [0.38–0.59]*** 0.10 [0.78–1.26]
Cardiovascular *** 0.42 [0.32–0.56]*** 1.23 [0.91–1.17]
Seizures *** 0.23 [0.08–0.91]* 0.045 [0.01–0.14]***
Acute *** 0.43 [0.35–0.53]*** 1.144 [0.91–1.43]
Long term · · ·
Acute and long term · · ·
Psychological adverse effects 0.85 [0.69–1.045] 0.92[0.74–1.14]
Anxiety *** 0.88 [0.71–1.10] 1.49[ 1.18–1.88]***
Paranoia *** 0.96 [0.72–1.27] 1.62 [1.20–2.20]**
Low mood *** 0.63 [0.42–0.94]* 0.33 [0.22–0.48]***
Acute ** 0.809 [0.65–1.001] 1.128 [0.90–1.41]
Long term ** · 0.88 [0.57–1.37]
Acute and long term * 0.35 [ 0.16–0.75]** 0.793 [0.40–1.58]
1790 Psychopharmacology (2022) 239:1783–1796
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1 3
Classification ofsubjective drug effects
The XGBoost algorithm was used to extract the optimal fea-
tures needed to classify the three canonical novel psychedelics.
The classification model considered each drug according to
self-reported scores of the 5 dimensions of the 5D-ASC. Fig-
ure3b presents the ROC curves; the corresponding values of
area under the curve (AUC) for each drug, translating to an
average macro-AUC of 0.79 [95% CI: 0.77–0.81] across all
testing folds. Figure3c represents the average main classifi-
cation metrics, combined into a common F1-score per class.
Based on a probability threshold of 50%, the sensitivi-
ties (precision) were approximately 82%, 65% and 50% in the
4-AcO-DMT, 1P-LSD and 2C-B models, respectively. The
specificities (recall) were approximately 67%, 69% and 54% in
4-AcO-DMT, 1P-LSD and 2C-B models, respectively. Macro-
averages for all classes yielded an average model accuracy
of 0.63 [95% CI: 0.61–0.64]. Sores for precision were 0.62
[95% CI: 0.61–0.64]; recall 0.62 [95% CI: 0.61–0.64] and
for a combined f1 of 0.62 [95% CI: 0.60–0.63]. Together,
these scores are symptoms of multiclass model performance
being driven by above chance separability of 4-AcO-DMT
and 1P-LSD across classification thresholds.
In this study, relative feature importance was calculated
during the XGBoost model creation, as defined by the
explained variance, each feature contributes to the decision
tree branch it resides on. No feature was deemed irrelevant.
Figure3d shows the result of deriving the importance of
the main features among all the explanatory variables. As
shown, the most important features were dread of ego disso-
lution and oceanic boundlessness. Visual restructualisation
contributed the least to the model.
Discussion
The present study aimed to provide information surrounding
individual NPs among 1180 adult users. By employing a
two-part structure, we were firstly able to collect informa-
tion regarding the prevalence, use profile and side effects
Fig. 3 Subjective effect scores and XGBoost model performance. a
Retrospective effects of 2C-B (N = 176), 4-AcO-DMT (N = 59) and
1P-LSD (N =
103) on the 5 major dimensions of 5D-ASC. Scores are
calculated as percentage maximum scores. Data points show means;
maximum score
=
100. Scores pertaining to the 11 subdimensions
of the 5D-ASC can be found in the supplementary materials. b ROC
curves show the superiority in classifying each drug against all others.
The corresponding values of AUC for each drug are presented, as well
as their individual classification report in (c). In (d), the most important
features of the XGBoost model are represented, as calculated by gain
1791Psychopharmacology (2022) 239:1783–1796
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1 3
of individual novel psychedelics, which allowed us to fur-
ther home in on a popular triad of canonical NPs: 2C-B,
4-AcO-DMT and 1P-LSD. Considering their popularity, we
collected comprehensive information on their experiential
elements. Each a representative of the three main structural
classes of psychedelics, we tapped into the detailed nature of
the 5D-ASC questionnaire using a supervised classification
technique to explore the distinguishability of their self-rated
perceptual profiles.
Our sample comprised of mostly male, well-educated
respondents who were previously acquainted with classi-
cal psychedelics and a range of NPs. Users most frequently
stated prior experience with phenylethylamine derivatives
(61.5%) with 2C-B representing the most frequently tried
drug of all (48.6%). Despite only reflecting a snapshot of
current NP use, our findings are consistent with prior epi-
demiological NPS surveys and EMCDDA novel psychoac-
tive substance seizures by law enforcement (Neicun etal.
2020). That said, users also reported consuming an average
of five different compounds. Written-in responses exem-
plify the continual evolution of NPs; with dihydrodifuran
analogue 2C-B-FLY, accounting for 26.4% of all pheneth-
ylamine write-ins, synthesised not-long after the publica-
tion of Shulgin’s pharmacopeia (Monte etal. 1997) and
1cP-LSD (45.7% of unlisted lysergamides) being detected
as recently as 2019 (Brandt etal. 2020). As governments
signal their intention to control a substance, wholesale pro-
ducers operating in legal grey areas can easily switch to
new, noncontrolled replacement holding similar effects to
its scheduled NP counterpart, either from scratch or from its
counterpart as a precursor (Francis and Smith 2022). Pair-
ing this understanding with varying national NP monitoring
infrastructure and legislation, write-in and phenethylamine
frequencies may reflect different national availabilities. For
example, whereas 2C-B has been scheduled since 2001 and
has since become an established substance such as MDMA,
2C-B-FLY is still unscheduled in the USA (de Boer and
Bosman 2004).
We gathered extensive use parameters for individual NPs.
Oral intake was the most favoured route of administration,
reflecting their frequent sale in oral formulations, such as
powders and tablets (Schmidt etal. 2011). Exemptions to the
rule appear with the sublingual use of lysergamide analogues
and ultra-potent NBOMes, often missold on blotter papers as
LSD (Bersani etal. 2014; Zawilska etal. 2020). Users also
reported diverse dosing ranges and durations of drug effects.
This variation is compounded by the fact users may simply
be unaware of the purity and/or the quantity of the dose
taken (Brunt etal. 2017). Regardless, phenethylamines show
distinctive pharmacodynamics, extending beyond the psy-
chedelic receptome. By increasing extracellular monoam-
ine concentrations through the inhibition of norepinephrine
(NET), dopamine (DAT) and serotonin (SERT) transporters,
they may result in a constellation of psychostimulant-like
effects outside of their hallucinogenic potential (Han and
Gu 2006). Prior work has attributed these features to greater
odds of physical harm (Sexton etal. 2019), findings which
we reiterate in the present study, as shown by higher ratings
of overall physical AEs and seizures. Legislative stances
towards NPs over the years have been largely guided by
high profile intoxication with NBOMEs and 2C-X deriva-
tives with similar clinical presentations such as tachycardia,
hypertension and convulsions (Dean etal. 2013; Palamar
etal. 2016a, b; Hondebrink etal. 2020). However, outside
of seizures, we are unable to quantify the severity of these
effects for users. For example, often a distinctive feature
of serotonergic psychedelics is the ‘body-load’; transient
somatic symptoms (nausea, discomfort, vomiting) which
often accompany the onset of their effects (Dos Santos etal.
2018).
Care should be taken prior to attributing patterns of spe-
cific psychological AEs, in light of our finding of no overall
significant differences. While one can state certain delete-
rious traits such as observed heightened anxiety following
lysergamide use, or low mood following phenethylamines
are accounted by differences in subjective high quality or
mood sequelae stemming from compound-dependent neuro-
toxicity (Zwartsen etal. 2018; Xu etal. 2019; Asanuma etal.
2020), psychedelic experiences often comprise volatile emo-
tional states (Brouwer and Carhart-Harris 2020). Observed
outcomes may therefore be defined by an amalgamation of
non-pharmacological contextual factors (Hartogsohn 2016).
Despite much having been written about the relative risks
of each class, it is currently unknown whether any hold
particular therapeutic benefits. Novel tryptamines such as
5-MeO-DMT and purportedly high-risk phenethylamines
such as DOI have been documented to be neuroplasticity-
inducing the latter appearing among surveyed microdosers
(Hutten etal. 2019). As such, prospective studies set prior
to use may be able to further clearer indices of their relative
harms and benefits while issues pertaining to ecology and
the legal status of NPs in such studies may be circumvented
by employing volunteer-orientated citizen-science designs
(Silvertown 2009) such as those used in microdosing studies
(Szigeti etal. 2021).
Functional differences in signalling cascades stemming
from structural differences are hypothesised to relate to the
unique nature of narrative drug experiences (Zamberlan etal.
2018). Using pairwise comparisons and a ML approach, we
demonstrated that despite users holding similar motivations
to use 2C-B, 4-AcO-DMT and 1P-LSD, the phenomenologi-
cal markers of these were not correspondent. Entheogenic
features such as oceanic boundlessness and dread of ego
dissolution were rated significantly less for 2C-B than for
4-AcO-DMT and 1P-LSD. Characterised as an entactogen
with psychedelic-like effects, observational studies have
1792 Psychopharmacology (2022) 239:1783–1796
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1 3
demonstrated 2C-B only produces mild psychedelic effects.
As with other entactogens such as 2C-E, 4-FA and MDMA,
its effects are limited to perceptual alterations and pseudo-
hallucinations (Papaseit etal. 2018; Kuypers etal. 2019;
Papaseit etal. 2020; Studerus etal. 2021). These descrip-
tions may be exemplified by the absence of dose-dependent
effects, endorsement of euphoria as a motivation by 49% of
users and its reiterated use at music events (Palamar etal.
2016a, b). Consequently, that what distinguishes certain
phenethylamines from tryptamines and lysergamides may
not be a question of experience quality, but rather depth. In
this study, our XGBoost approach served as a useful proof
of concept for the distinction of drug effects using ML. Our
model yielded better class prediction for 4-AcO-DMT and
1P-LSD, with the former showing the highest specificity and
sensitivity. Looking to their pharmacology, each is seem-
ingly prodrugs of their respective progenitor’s psilocybin
and LSD, and is reported to produce comparable effects to
their predecessors (Coney etal. 2017; Palamar and Acosta
2020). 4-AcO-DMT (O-Acetylpsilocin) is an acetylated
equivalent of psilocybin’s primary bioactive metabolite
psilocin (Madsen etal. 2019) previously suggested to be a
suitable substitute for psilocybin for clinical use (Nichols
1999), with 1P-LSD (1-Propionyl-d-lysergic acid diethyla-
mide) similarly hydrolysed to LSD upon intake (Brandt etal.
2016; Grumann etal. 2020). These findings may extend to
other closely related homologues such as 4-HO-MET or
ALD-52, albeit with variable potency. Users choosing these
substances may therefore be guided by their accessibility and
familiarity of effects. Pointing to this, 20% of 4-AcO-DMT
users and 19.7% of 1P-LSD in our study reported using them
as legal substitutes for classical compounds.
The results reported herein should be considered in the
light of some key limitations. Our self-selected homogenous
sample of NP users may not be representative of the general
population. Whereas we collected contextual demographic
variables for reference and controlled for age and sex in
our regression analyses, larger cross-sectional studies spe-
cifically aimed at collecting unexamined sociodemographic
variables such as ethnicity and income may prove to dem-
onstrate their influence on AE outcomes. Furthermore, NP
prevalence may vary according to the study timeframe,
user location and degree of prior experience. As such, sub-
sets of experienced use may have clouded our capacity to
detect class-specific associations and/or inflated dosages.
While precautions were taken during XGboost develop-
ment to diminish model variability, several caveats should
be considered. Training was performed on a small subset of
users, in the absence of external validation with an exter-
nal independent sample for each compound, nor was the
predictive viability of the 5D-ASC cross-examined. Adding
to this, resampling methods such as SMOTE do not take
into consideration that neighbouring examples can be from
other classes, which may further diminish the occurrence of
useful edge cases. While our chosen trio hold close struc-
tural and phenomenological resemblance to other members
of their NP class, our examination is a first steppingstone
towards more comprehensive evaluations. Future work will
assess model specificity across a greater range of NP repre-
sentatives, including exemptions to the rule such as DiPT,
reported to primarily produce auditory distortions (Carbon-
aro etal. 2013).
In conclusion, the present work provides a dictionary of
use characteristics for structurally independent novel psych-
edelics and demonstrates NPs may be discerned by their
entheogenic properties. Future legislative approaches should
take into consideration the overlapping nature of novel hom-
ologues with classical predecessors of clinical use. Work
should continue to establish reference points to salient NP
subclasses, as to confirm the veracity of these findings. Fol-
low-up studies should aim to employ a dual-prong fishing
approach in the form of online surveys harmonising free
narratives alongside validated retrospective assessments for
a particular compound.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00213- 022- 06142-4.
Acknowledgements The authors would like to thank the people who
advertised this survey on their websites (https:// psych onaut wiki. org/,
https:// www. stich tingo pen. nl/) and all respondents for their time and
effort.
Author contribution J.R, N.M and P.M were responsible for study
conceptualisation. Primary data analyses and model development
were performed by P.M, the former assisted by F.V. N.M and P.M
were responsible for drafting the first manuscript. Remaining authors
assisted with analysis interpretation and manuscript editing. All authors
approve the submission of this manuscript.
Funding This study was supported by the Dutch Research Council
(NWO, grant number 406.18.GO.019.
Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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1 3
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