Do heroin metabolites appear in solid drug samples?

Objective

To determine how common it is for 6-MAM and 3-MAM to appear in solid street drug samples. The motivation is that 6-MAM and 3-MAM are considered downstream biological/physiological metabolites used to identify exposure to heroin in post-mortem toxicology. In the human body, heroin is metabolized to 6-MAM (and then to morphine).

Analysis was conducted on June 20, 2024 to answer an internal team question about classification of polysubstance overdose deaths.

Summary of Findings

6-MAM (and much less frequently 3-MAM) were found in 413 samples; by comparison there were 875 heroin samples. 46% of heroin samples also contained 6-MAM, but in n=9 samples 6-MAM was found without heroin present. Acetylcodeine was the next most commonly found substance in samples containing 6-MAM.

By geography, 6-MAM was mostly commonly found in heroin in TX (93%), CA, AZ in the top tier, followed by WA and OR (63%). The following tier was NC (43%), MI (35%), and NY (35%).

Of samples containing 6-MAM, the form was black tar heroin for 33% (all heroin samples), going up to 45% (6-MAM in primary abundance only).

Solid drug samples routinely contain 6-MAM. In 9/413 samples, 6-MAM was found without heroin.

Alternate hypothesis considered

We also wanted to know if detection of 6-MAM could be an artifact of the GCMS-based detection method. Upon consulting with our analytical chemist, we believe that 6-MAM was present in the original samples because there are many heroin samples without 6-MAM and because the abundance of 6-MAM varies by sample; typical GC breakdown products appear consistently along with the parent molecule, and tend to be detected in consistent proportional abundance.

Semantic clarification

The word metabolite used in post-mortem toxicology (or urine analysis) is used to indicate a molecule that has been altered from a parent substance due to physiological processes (e.g., metabolism in the human body) often by enzymatic action. In analytical and synthetic chemistry, the word metabolite is used more broadly to indicate downstream products generated by chemical processes. While similar in concept, we refer to metabolite in the first sense because of the context of our research motivation regarding post-mortem toxicology.

Methods

We obtain drug samples from 140+ harm reduction programs, clinics, hospitals, EMS, health departments, and drug user unions in 35 US states, covering about 200 counties. Samples are donated voluntarily, and collected anonymously. Mailed-in samples are analyzed using exactive (high resolution) GCMS, with untargeted search, and pure standard verification of identified substances. See laboratory methods and codebook and GitHub repo for code and dataset, and further details.

🏷️ Two data caveats: We get more more samples from NC, WA, NY, CA, and MI than other states. Geographic data below should not be construed as prevalence. And people may preferentially send us samples because they caused unusual reactions. There is no possible data source on street drugs that is fully generalizable. Got it? Okay!

import pandas as pd # Import data from .dta files lab = pd.read_stata('lab_detail.dta') card = pd.read_stata('analysis_dataset.dta') # Function to get overlapping columns def get_overlapping_columns(df1, df2): return [col for col in df1.columns if col in df2.columns and col != 'sampleid'] # Drop overlapping columns from card dataframe card = card.drop(columns=get_overlapping_columns(card, lab)) # Merge data and remove suffixes for overlapping column names merged = pd.merge(card, lab, on='sampleid', how='left', suffixes=(None, None))

Results

All Sample origins

How many solid drug samples with heroin metabolites have been detected?

# Filter the merged dataframe for samples containing 6-MAM or 3-MAM heroin_metabolites = merged[merged['substance'].str.contains('6-MAM|3-MAM', na=False)] # Count the unique sampleid unique_heroin_metabolites_count = heroin_metabolites['sampleid'].nunique() print(f'There are {unique_heroin_metabolites_count} unique samples that contain 6-MAM or 3-MAM.')

from tabulate import tabulate # Define the substances of interest substances = ['6-MAM', '3-MAM', 'heroin'] # Initialize a list to store the results results = [] # Calculate counts and percentages for each substance for substance in substances: count = merged[merged['substance'].str.contains(substance, na=False)]['sampleid'].nunique() percent = (count / unique_sampleid_count) * 100 results.append([substance, count, f'{percent:.1f}%']) # Create a summary table using tabulate table_headers = ['Substance', 'Count', 'Percent'] print(tabulate(results, headers=table_headers, tablefmt='grid'))

Was 3-MAM always found with 6-MAM?

# Check if 3-MAM was always found with 6-MAM in the samples samples_3_mam = set(merged[merged['substance'].str.contains('3-MAM', na=False)]['sampleid'].unique()) samples_6_mam = set(merged[merged['substance'].str.contains('6-MAM', na=False)]['sampleid'].unique()) # Find the intersection of the two sets samples_with_both = samples_3_mam.intersection(samples_6_mam) # Check if all samples with 3-MAM also have 6-MAM all_3_mam_with_6_mam = samples_3_mam.issubset(samples_6_mam) all_3_mam_with_6_mam, len(samples_with_both), len(samples_3_mam), len(samples_6_mam)

Yes, 3-MAM was always found with 6-MAM. There are 12 samples with 3-MAM, and all of them also contain 6-MAM.

What percent of heroin samples contained 6-mam?

# Calculate the percentage of heroin samples that contained 6-MAM # Get the set of sample IDs that contain heroin samples_heroin = set(merged[merged['substance'].str.contains('heroin', na=False)]['sampleid'].unique()) # Get the set of sample IDs that contain 6-MAM samples_6_mam = set(merged[merged['substance'].str.contains('6-MAM', na=False)]['sampleid'].unique()) # Find the intersection of the two sets to get samples that contain both heroin and 6-MAM samples_heroin_with_6_mam = samples_heroin.intersection(samples_6_mam) # Calculate the percentage percent_heroin_with_6_mam = (len(samples_heroin_with_6_mam) / len(samples_heroin)) * 100 percent_heroin_with_6_mam

What else was found with 6-MAM and 3-MAM?

# Filter the merged DataFrame for samples containing exactly the selected substance selected_samples = merged[merged['substance'].str.contains('6-MAM|3-MAM', na=False)]['sampleid'].unique() # Filter the merged DataFrame for the selected samples samples_with_selected_substance = merged[merged['sampleid'].isin(selected_samples)] # Group by sampleid and list all substances found in each sample substances_per_sample = samples_with_selected_substance.groupby('sampleid')['substance'].apply(lambda x: list(x)).reset_index() # Filter out samples with only the selected substance substances_per_sample['substance_count'] = substances_per_sample['substance'].apply(len) substances_per_sample_filtered = substances_per_sample[substances_per_sample['substance_count'] > 1] # Calculate the frequency of each substance found with the selected substance substance_frequency = substances_per_sample_filtered.explode('substance')['substance'].value_counts().reset_index() substance_frequency.columns = ['Substance', 'Frequency'] # Sort by frequency substance_frequency_sorted = substance_frequency.sort_values(by='Frequency', ascending=False) # Suppress row for the selected substance substance_frequency_sorted = substance_frequency_sorted[~substance_frequency_sorted['Substance'].str.contains('6-MAM|3-MAM', na=False)] # Display the sorted substance frequency table substance_frequency_sorted

Were there any samples that contained 6-MAM or 3-MAM that didn't also contain heroin?

# Filter for samples that contain 6-MAM or 3-MAM but do not contain heroin samples_without_heroin = substances_per_sample_filtered[~substances_per_sample_filtered['substance'].apply(lambda x: 'heroin' in x)] # Count the number of such samples samples_without_heroin_count = samples_without_heroin['sampleid'].nunique() print(f'There are {samples_without_heroin_count} samples that contained 6-MAM or 3-MAM but did not contain heroin.')

For samples containing 6-MAM and 3-MAM, what were the collection methods?

We want to get a sense if these samples were collected as powder (scoop) versus residue from pipes/syringes to reduce the chance of cross-contamination influencing our inference.

# Filter the merged DataFrame for samples containing 6-MAM or 3-MAM heroin_metabolites_samples = merged[merged['substance'].str.contains('6-MAM|3-MAM', na=False)] # Aggregate table with counts and percents for collection methods collection_methods_counts = heroin_metabolites_samples['collection'].value_counts().reset_index() collection_methods_counts.columns = ['Collection Method', 'Count'] collection_methods_counts['Percent'] = (collection_methods_counts['Count'] / collection_methods_counts['Count'].sum()) * 100 # Display the aggregated table collection_methods_counts

About half the samples were spatula which gives us more confidence in the findings. But surprising to see 2 pills containing the heroin metabolites! Let's look into that.

Fake pills with heroin metabolites?

The sample IDs for the 2 pills containing 6-MAM or 3-MAM are `300675` and `500106`.

Sample 300675 was contamination of a fake flualprazolam pill.

Major substances in graph: Peak 10.19 = fentanyl Peak 6.64 = acetaminophen Peak 9.35 = 6-monoacetylmorphine (6-MAM)

Looking at the chromatogram, 6-MAM was in relatively less abundance, similar to heroin and acetylcodeine. The designation of trace vs. primary abundance is <5% peak height area of the primary detected substance (relative abundance on vertical axis). So, it is worth looking into how much of the 6-MAM and 3-MAM was detected by abundance.

What was the relative abundance of heroin metabolites?

# Import tabulate from tabulate import tabulate # Replace NaN values and blank strings in the 'abundance' column with 'primary' heroin_metabolites_samples.loc[:, 'abundance'] = heroin_metabolites_samples['abundance'].replace('', 'primary').fillna('primary') # Aggregate table with counts and percents for abundance abundance_counts = heroin_metabolites_samples['abundance'].value_counts().reset_index() abundance_counts.columns = ['Abundance', 'Count'] abundance_counts['Percent'] = ((abundance_counts['Count'] / abundance_counts['Count'].sum()) * 100).round(1).astype(str) + '%' # Display the aggregated table using tabulate, but do not display the first column print(tabulate(abundance_counts.values, headers=abundance_counts.columns, tablefmt='grid'))

6-MAM and 3-MAM concentrations are in primary abundance in about ⅓ of samples that contain them.

Where are heroin metabolites found in the US?

from tabulate import tabulate # Round normalized_count to 1 decimal place state_counts_merged['normalized_count'] = state_counts_merged['normalized_count'].round(1) # Display the state counts and percents using tabulate state_counts_merged_sorted = state_counts_merged.sort_values(by='normalized_count', ascending=False) # Do not display the first column print(tabulate(state_counts_merged_sorted.values, headers=state_counts_merged_sorted.columns, tablefmt='grid'))

So it looks like 6-MAM may show up more in areas where black tar heroin is present. Some states had few samples, limiting inference.

What type of heroin (tar or powder) is 6-MAM found in?

Let's look at it first with all 6-MAM samples.

# Remake texture category based on new instructions samples_6_mam_df.loc[:, 'texture_category'] = samples_6_mam_df['texture'].apply(lambda x: 'powder' if any(s in x.lower() for s in ['powder', 'rock']) else ('tar' if any(s in x.lower() for s in ['tar', 'wax']) else 'other')) # Create an aggregate table for texture category with counts and percents texture_counts = samples_6_mam_df['texture_category'].value_counts().reset_index() texture_counts.columns = ['Texture Category', 'Count'] texture_counts['Percent'] = ((texture_counts['Count'] / texture_counts['Count'].sum()) * 100).round(1).astype(str) + '%' # Add total row to the texture_counts DataFrame texture_counts.loc['Total'] = texture_counts[['Count']].sum() texture_counts.loc['Total', 'Texture Category'] = 'Total' texture_counts.loc['Total', 'Percent'] = '100%' # Display the aggregated table using tabulate from tabulate import tabulate print(tabulate(texture_counts.values, headers=texture_counts.columns, tablefmt='grid'))

Now let's look by where 6-MAM is only in primary abundance.

# Filter the samples_6_mam_df DataFrame for samples where 6-MAM is in primary abundance samples_6_mam_primary = samples_6_mam_df[samples_6_mam_df['abundance'] == ''] # Create an aggregate table for texture category with counts and percents texture_counts_primary = samples_6_mam_primary['texture_category'].value_counts().reset_index() texture_counts_primary.columns = ['Texture Category', 'Count'] texture_counts_primary['Percent'] = ((texture_counts_primary['Count'] / texture_counts_primary['Count'].sum()) * 100).round(1).astype(str) + '%' # Add total row to the texture_counts DataFrame texture_counts_primary.loc['Total'] = texture_counts_primary[['Count']].sum() texture_counts_primary.loc['Total', 'Texture Category'] = 'Total' texture_counts_primary.loc['Total', 'Percent'] = '100%' # Display the aggregated table using tabulate from tabulate import tabulate print(tabulate(texture_counts_primary.values, headers=texture_counts_primary.columns, tablefmt='grid'))

So it appears that 6-MAM shows up in primary abundance in both powder and tar samples.

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}Do heroin metabolites appear in solid drug samples?