Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach

Introduction

Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.

Methods

We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs.

Results

Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43).

Discussion

Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Painful Convictions: Examining Pain Management Malpractice Claims From Incarcerated Patients, 2000-2020

We aim to characterize the legal landscape of incarcerated patients' pain management malpractice claims and to discuss the ethical and policy implications that result. The most common rationales for lawsuits were failure to completely treat (38 [46.3%]), failure to offer (34 [41.4%]), and delay of treatment (6 [7.3%]). In cases won by defendants, the most common rationale for verdicts was no deliberate indifference occurred (74 [86.6%]). We found that incarcerated individuals were often unsuccessful in litigating claims for inadequate pain management despite several cases pointing toward treatment strategies far below what would be ethically accepted as standard of care in the community setting.

Diagnostic contribution of contrast-enhanced 3D MR imaging of peripheral nerve pathology

OBJECTIVE

To assess the diagnostic contribution of contrast-enhanced 3D STIR (ce3D-SS) high-resolution magnetic resonance (MR) imaging of peripheral nerve pathology relative to conventional 2D sequences.

MATERIALS AND METHODS

In this IRB-approved retrospective study, two radiologists reviewed 60 MR neurography studies with nerve pathology findings. The diagnostic contribution of ce3D-SS imaging was scored on a 4-point Likert scale (1 = no additional information, 2 = supports interpretation, 3 = moderate additional information, and 4 = diagnosis not possible without ce3D-SS). Image quality, nerve visualization, and detection of nerve pathology were also assessed for both standard 2D neurography and ce3D-SS sequences utilizing a 3-point Likert scale. Descriptive statistics are reported.

RESULTS

The diagnostic contribution score for ce3D-SS imaging was 2.25 for the brachial plexus, 1.50 for extremities, and 1.75 for the lumbosacral plexus. For brachial plexus, the mean consensus scores for image quality, nerve visualization, and detection of nerve pathology were 2.55, 2.5, and 2.55 for 2D and 2.35, 2.45, and 2.45 for 3D. For extremities, the mean consensus scores for image quality, nerve visualization, and detection of nerve pathology were 2.60, 2.80, and 2.70 for 2D and 1.8, 2.20, and 2.10 for 3D. For lumbosacral plexus, the mean consensus scores for image quality, nerve visualization, and detection of nerve pathology were 2.45, 2.75, and 2.65 for 2D and 2.0, 2.45, and 2.25 for 3D.

CONCLUSION

Overall, our study supports the potential application of ce3D-SS imaging for MRN of the brachial plexus but suggests that 2D MRN protocols are sufficient for MRN of the extremities and lumbosacral plexus.

802 An electronic health record-based approach to identify and characterize patients with immune checkpoint inhibitor-associated arthritis

Background

Immune checkpoint inhibitors (ICIs) are a pillar of cancer therapy with demonstrated efficacy in a variety of malignancies. However, they are associated with immune-related adverse events (irAEs) that affect many organ systems with varying severity, inhibiting patient quality of life and in some cases the ability to continue immunotherapy. Research into irAEs is nascent, and identifying patients with adverse events poses a critical challenge for future research efforts and patient care. This study's objective was to develop an electronic health record (EHR)-based model to identify and characterize patients with ICI-associated arthritis (checkpoint arthritis).

Methods

Forty-two patients with checkpoint arthritis were chart abstracted from a cohort of all patients who received checkpoint therapy for cancer (n=2,612) in a single-center retrospective study. All EHR clinical codes (N=32,198) were extracted including International Classification of Diseases (ICD)-9 and ICD-10, Logical Observation Identifiers Names and Codes (LOINC), RxNorm, and Current Procedural Terminology (CPT). Logistic regression, random forest, gradient boosting, support vector machine, K-nearest neighbors, and neural network machine learning models were trained to identify checkpoint arthritis patients using these clinical codes. Models were evaluated using receiver operating characteristic area under the curve (ROC-AUC), and the most important variables were determined from the logistic regression model. Models were retrained on smaller fractions of the important variables to determine the minimum variable set necessary to achieve accurate identification of checkpoint arthritis.

Results

Logistic regression and random forest were the highest performing models on the full variable set of 32,198 clinical codes (AUCs: 0.911, 0.894, respectively) (table 1). Retraining the models on smaller fractions of the most important variables demonstrated peak performance using the top 31 clinical codes, or 0.1% of the total variables (figure 1). The most important features included presence of ESR, CRP, rheumatoid factor lab, prednisone, joint pain, creatine kinase lab, thyroid labs, and immunization, all positively associated with checkpoint arthritis (figure 2).

Conclusions

Our study demonstrates that a data-driven, EHR based approach can robustly identify checkpoint arthritis patients. The high performance of the models using only the 0.1% most important variables suggests that only a small number of clinical attributes are needed to identify these patients. The variables most important for identifying checkpoint arthritis included several unexpected clinical features, such as thyroid labs and immunization, indicating potential underlying irAE associations that warrant further exploration. Finally, the flexibility of this approach and its demonstrated effectiveness could be applied to identify and characterize other irAEs.

Ethics Approval

This study was approved by the Northwestern University Institutional Review Board, ID STU00210502, with a granted waiver of consent

Abstract 802 Table 1

Model performance metrics

AUC was calculated from the ROC curve. Sensitivity, specificity, PPV, and NPV were determined at the threshold maximizing the F1-score. AUC = area under the curve, ROC = receiver operating characteristic, PPV = positive predictive value, NPV = negative predictive value

Abstract 802 Figure 1

Model AUC trained on decreasing fractions of the most important variables, determined by the random forest model. 100% = 32,198 clinical codes. LReg = logistic regression, RF = random forest, GB = gradient boosting, NN = neural network, KNN = K-nearest neighbor, SVM = support vector machine, SVMAnom = SVM anomaly detection

Abstract 802 Figure 2

The 31 most important variables determined by the logistic regression (A, coefficients) and random forest (B, relative importance) models

Integrated genomic analyses of cutaneous T-cell lymphomas reveal the molecular bases for disease heterogeneity

Cutaneous T-cell lymphomas (CTCLs) are a clinically heterogeneous collection of lymphomas of the skin-homing T cell. To identify molecular drivers of disease phenotypes, we assembled representative samples of CTCLs from patients with diverse disease subtypes and stages. Via DNA/RNA-sequencing, immunophenotyping, and ex vivo functional assays, we identified the landscape of putative driver genes, elucidated genetic relationships between CTCLs across disease stages, and inferred molecular subtypes in patients with stage-matched leukemic disease. Collectively, our analysis identified 86 putative driver genes, including 19 genes not previously implicated in this disease. Two mutations have never been described in any cancer. Functionally, multiple mutations augment T-cell receptor-dependent proliferation, highlighting the importance of this pathway in lymphomagenesis. To identify putative genetic causes of disease heterogeneity, we examined the distribution of driver genes across clinical cohorts. There are broad similarities across disease stages. Many driver genes are shared by mycosis fungoides (MF) and Sezary syndrome (SS). However, there are significantly more structural variants in leukemic disease, leading to highly recurrent deletions of putative tumor suppressors that are uncommon in early-stage skin-centered MF. For example, TP53 is deleted in 7% and 87% of MF and SS, respectively. In both human and mouse samples, PD1 mutations drive aggressive behavior. PD1 wild-type lymphomas show features of T-cell exhaustion. PD1 deletions are sufficient to reverse the exhaustion phenotype, promote a FOXM1-driven transcriptional signature, and predict significantly worse survival. Collectively, our findings clarify CTCL genetics and provide novel insights into pathways that drive diverse disease phenotypes.

Advances in the Translational Science of Dermatitis

The cycle of converting mechanistic insight into therapeutic interventions is called translational science. It has been relatively sluggish in atopic dermatitis (AD), but finally pathomechanisms have been identified and therapeutic targets selected and refined. From inflammatory mediators, skin barrier enhancement, itch relief, and alteration of the microbiota, several therapies have been proposed and are actively being studied for AD, suggesting an end to the drought of innovation.

Readability of online patient education materials for juvenile dermatomyositis

In this study, we sought to analyze the readability of online patient education materials (PEMs) related to juvenile dermatomyositis (JDM). We analyzed the top 100 Google results and using six different readability scores, found 53 PEMs which had an average grade reading level of 17.4 (graduate level). PEMs by health care providers were written at higher grade levels than those by non-health care providers. Our findings demonstrate a clear need for online JDM PEMs that are written at an appropriate reading level and can be comprehended by patients and families of all levels of health literacy.

The Effect of Homelessness on Patient Wait Times in the Emergency Department

BACKGROUND

Prolonged emergency department (ED) wait times could potentially lead to increased morbidity and mortality. While previous work has demonstrated disparities in wait times associated with race, information about the relationship between experiencing homelessness and ED wait times is lacking.

OBJECTIVES

The purpose of this study was to explore the relationship between residence status (undomiciled vs. domiciled) and ED wait times. We hypothesized that being undomiciled would be associated with longer wait times.

METHODS

We obtained data from the National Hospital Ambulatory Medical Care Survey from 2014 to 2017. We compared wait times in each triage category using t tests. We used multivariate linear regression to explore associations between residence status and wait times while controlling for other patient- and hospital-level variables.

RESULTS

On average, undomiciled patients experienced significantly longer mean ED wait times than domiciled patients (53.4 vs. 38.9 min; p < 0.0001). In the multivariate model, undomiciled patients experienced significantly different wait times by 15.5 min (p = 0.0002). Undomiciled patients experienced increasingly longer waits vs. domiciled patients for the emergent and urgent triage categories (+33.5 min, p < 0.0001, and +22.7 min, p < 0.0001, respectively).

CONCLUSIONS

Undomiciled patients experience longer ED wait times when compared with domiciled patients. This disparity is not explained by undomiciled patients seeking care in the ED for minor illness, because the disparity is more pronounced for urgent and emergent triage categories.

US Prescription trends of antihistamines for atopic dermatitis, 2011-2016

Antihistamine use for primary treatment of atopic dermatitis (AD) is not recommended, but current guidelines state that sedating antihistamines are favored over non-sedating antihistamines for relief of burdensome pruritus. We analyzed the National Ambulatory Medical Care Survey data to compare use of antihistamines between dermatologists and non-dermatologists. Overall, dermatologists are more likely to prescribe sedating than non-sedating antihistamines when compared to non-dermatologists (P < .001, δ_abs  = 0.45). Patients under 21 years old (P = .03, δ_abs  = 0.10) and Black patients (P < .001, δ_abs  = 0.19) were also more likely to receive sedating antihistamines than non-sedating antihistamines. These findings highlight the differential prescribing practices for atopic dermatitis among physicians.

Lung Cancer Survival in Patients With Autoimmune Disease

IMPORTANCE

Patients with autoimmune disease and lung cancer pose a multidisciplinary treatment challenge, particularly with the advent of immunotherapy. However, the association between autoimmune disease and lung cancer survival is largely unknown.

OBJECTIVE

To determine the association between autoimmune disease and lung cancer survival.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study between 2003 and 2019 at a single academic medical center (Northwestern University). A query of the Northwestern Medicine Enterprise Data Warehouse identified 349 patients with lung cancer and several autoimmune diseases. Types of lung cancers included small cell, adenocarcinoma, squamous cell carcinoma, non-small cell not otherwise specified, and large cell carcinoma. Autoimmune diseases included rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, mixed connective tissue disease, myositis, and Sjögren syndrome. Inclusion criteria were biopsy-confirmed lung cancer, autoimmune diagnosis confirmed by a rheumatologist, and death or an encounter listed in the electronic medical record within 2 years of study end. A control group of patients with biopsy-proven lung cancer but without autoimmune disease was identified. Data analysis was conducted from March to July 2020.

EXPOSURE

Presence of autoimmune disease.

MAIN OUTCOMES AND MEASURES

Overall survival and progression-free survival in patients with autoimmune disease. The hypothesis was that patients with autoimmune disease would have worse progression-free survival and overall survival compared with patients in the control group.

RESULTS

Of the original 349 patients, 177 met inclusion criteria. Mean (SD) age at lung cancer diagnosis was 67.0 (10.0) years and 136 (76.8%) were women. Most common autoimmune diseases were rheumatoid arthritis (97 [54.8%]), systemic sclerosis (43 [24.3%]), and systemic lupus erythematous (15 [8.5%]). Most common lung cancers were adenocarcinoma (99 [55.9%]), squamous cell carcinoma (29 [16.4%]), and small cell lung cancer (17 [9.6%]). A total of 219 patients (mean [SD] age at diagnosis, 65.9 [4.1] years; 173 [79.0%]) were identified as having lung cancer without autoimmune disease and included in the control cohort. Compared with patients in the control group, patients with autoimmune disease experienced no difference in overall survival (log-rank P = .69). A total of 126 patients (69.5%) with autoimmune disease received standard of care vs 213 patients (97.3%) in the control group (P < .001). No individual autoimmune disease was associated with worse prognosis, even among patients with underlying interstitial lung disease.

CONCLUSIONS AND RELEVANCE

Compared with institutional controls, patients with autoimmune disease experienced no difference in survival despite the fact that fewer patients in this group received standard-of-care treatment. No individual autoimmune disease was associated with worse prognosis. Future multicenter prospective trials are needed to further evaluate autoimmune disease and lung cancer survival.

A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases

Dermatological diagnosis remains challenging for nonspecialists because the morphologies of primary skin lesions widely vary from patient to patient. Although previous studies have used artificial intelligence (AI) to classify lesions as benign or malignant, there have not been extensive studies examining the use of AI on identifying and categorizing a primary skin lesion's morphology. In this study, we evaluate the performance of a standalone AI tool to correctly categorize a skin lesion's morphology from a test bank of images. To provide a marker of performance, we evaluate the accuracy of primary care physicians to categorize skin lesion morphology in the same test bank of images without any aids and then with the aid of a simple visual guide. The AI system achieved an accuracy of 68% in determining the single most likely morphology from the test image bank. When the AI's top prediction was broadened to its top three most likely predictions, accuracy improved to 80%. In comparison, the diagnostic accuracy of primary care physicians was 36% without any aids and 68% with the visual guide (P < 0.001). The AI was subsequently tested on an additional set of 222 heterogeneous images of varying Fitzpatrick skin types and achieved an overall accuracy of 70% in the Fitzpatrick I-III skin type group and 68% in the Fitzpatrick IV-VI skin type group (P = 0.79). An AI is a powerful tool to assist physicians in the diagnosis of skin lesions while still requiring the user to critically consider other possible diagnoses.

Off-Label studies on anakinra in dermatology: a review

Purpose: Anakinra (Kineret^®) is an interleukin-1 receptor antagonist (IL-1Ra) FDA approved for use in rheumatoid arthritis and in neonatal-onset multisystem inflammatory disease (NOMID). It has been used off-label for a variety of dermatologic conditions. A review of the available studies and cases of these off-label uses would be valuable to the dermatologist considering alternative treatments for these oftentimes poorly studied conditions.Materials and methods: The PubMed/MEDLINE, EMBASE, Scopus, and ClinicalTrials.gov databases were searched with the term 'anakinra.' Results were manually screened to identify published data on off-label uses of anakinra in dermatologic conditions and systemic conditions with prominent dermatologic manifestations.Results: Anakinra appears to show efficacy for numerous dermatologic conditions, with the strongest evidence for hidradenitis suppurativa, Bechet's disease, Muckle-Wells syndrome, and SAPHO syndrome. Case reports and case series data are available for numerous other dermatologic conditions.Conclusion: Anakinra is a potential option for patients with certain difficult-to-treat dermatologic diseases, given its relatively benign adverse effect profile and its effectiveness in a wide array of conditions. Overall, anakinra appears to be a promising option in the treatment of numerous dermatologic inflammatory conditions refractory to first line therapies, but further and higher-quality data is needed to clarify its therapeutic role.

Cellular origins and genetic landscape of cutaneous gamma delta T cell lymphomas

Primary cutaneous γδ T cell lymphomas (PCGDTLs) represent a heterogeneous group of uncommon but aggressive cancers. Herein, we perform genome-wide DNA, RNA, and T cell receptor (TCR) sequencing on 29 cutaneous γδ lymphomas. We find that PCGDTLs are not uniformly derived from Vδ2 cells. Instead, the cell-of-origin depends on the tissue compartment from which the lymphomas are derived. Lymphomas arising from the outer layer of skin are derived from Vδ1 cells, the predominant γδ cell in the epidermis and dermis. In contrast, panniculitic lymphomas arise from Vδ2 cells, the predominant γδ T cell in the fat. We also show that TCR chain usage is non-random, suggesting common antigens for Vδ1 and Vδ2 lymphomas respectively. In addition, Vδ1 and Vδ2 PCGDTLs harbor similar genomic landscapes with potentially targetable oncogenic mutations in the JAK/STAT, MAPK, MYC, and chromatin modification pathways. Collectively, these findings suggest a paradigm for classifying, staging, and treating these diseases.

The relationship between the number of available therapeutic options and government payer (medicare part D) spending on topical drug products

The cost of prescription drugs has increased at rates far exceeding general inflation in recent history, with topical drugs increasing at a disproportionate rate compared to other routes of administration. We assessed the relationship between net changes in the number of therapeutic options, defined as any approved drug or therapeutic equivalent on the market, and prescription topical drug spending. Drugs were divided based on the category of use through pairing of Medicare Part D Prescriber Public Use and Food and Drug Administration (FDA) approved drug products databases. Across drug classes, we modeled the log of the ratio of total spending per unit in 2015 to total spending per unit in 2011 as a linear function of net number of topical therapeutic options over this time period. Primary outcomes include total Medicaid Part D spending on topical drugs and net change in the number of available therapeutic options within each category of use. Total spending on topical drugs increased by 61%, while the number of units dispensed increased by only 18% from 2011-2015. The greatest total spending increases were in categories with few new therapeutic options, such as topical corticosteroid and antifungal medications. Each net additional therapeutic option during 2011-2015 was associated with an reduction in how much relative spending per unit increased (95% CI 2.5%-14.4%, p = 0.013). Stimulating greater competition through increasing the net number of therapeutic options within each major topical category of use may place downward pressure on topical prescription drug spending under medicare Part D.

Off-label studies on tofacitinib in dermatology: a review

PURPOSE

Tofacitinib citrate is an oral Janus kinase 1/3 inhibitor approved for rheumatoid arthritis, ulcerative colitis, and active psoriatic arthritis. Tofacitinib is being increasingly used off-label for dermatological conditions, with varying efficacy across recent studies. A review of these studies will be a helpful resource for dermatologists considering the use of tofacitinib for conditions refractory to first-line therapies.

MATERIALS AND METHODS

MEDLINE, Embase, CINAHL Plus, Cochrane Library, Scopus, Web of Science, Clinicaltrials.gov, and the WHO International Clinical Trials Registry Platform were all searched for articles and trials mentioning the term 'tofacitinib', then manually reviewed to identify published data on off-label uses of tofacitinib. The article was structured according to the quality of the evidence available.

RESULTS

Tofacitinib appears to show strong efficacy for numerous dermatologic conditions. Randomized controlled trial data is available for atopic dermatitis, alopecia areata, and plaque psoriasis. Case report and case series data is available for numerous other dermatologic conditions.

CONCLUSION

While tofacitinib has a wide array of immunoregulatory properties, making it a possible candidate for treating many dermatologic conditions refractory to other treatments, further testing is needed to better characterize its efficacy and utility moving forward, as well as its safety and adverse effect profile.

Dupilumab in Dermatology: Potential for Uses Beyond Atopic Dermatitis

Dupilumab inhibits the interleukin-4 receptor subunit α and is FDA approved for treatment of moderate-to-severe atopic dermatitis. It is a relatively new drug, and whether it is efficacious for other diseases in dermatology is an area of increasing interest. We searched the literature and ClinicalTrials.gov database for uses of dupilumab beyond atopic dermatitis in dermatology and for ongoing studies on new uses for dupilumab. Off-label reports identified described use of dupilumab for several different dermatologic conditions, including allergic contact dermatitis, hand dermatitis, chronic spontaneous urticaria, prurigo nodularis, and alopecia areata. Overall, there is limited but promising data for dupilumab use beyond atopic dermatitis in dermatology. The relatively safe adverse effect profile of dupilumab may make it an option for certain recalcitrant diseases in dermatology, but further studies will be needed to assess its efficacy and determine its best possible use. J Drugs Dermatol. 2019;18(10):1053-1055.

Off-label studies on apremilast in dermatology: a review

Purpose: Apremilast is a phosphodiesterase-4 inhibitor FDA approved for psoriatic arthritis and moderate to severe plaque psoriasis. In recent years, multiple studies have suggested other potential uses for apremilast in dermatology. A summary of these various studies will be a valuable aid to dermatologists considering apremilast for an alternative indication.Materials and methods: The PubMed/MEDLINE and ClinicalTrials.gov databases were queried with the term 'apremilast,' with results manually screened to identify published data on off-label uses of apremilast. The article was structured by the quality of evidence available.Results: Apremilast use in dermatology beyond plaque psoriasis and psoriatic arthritis is frequently described in the literature, with a mixture of positive and negative results. Randomized controlled data is available for Behçet's disease, hidradenitis suppurativa, nail/scalp/palmoplantar psoriasis, alopecia areata, and atopic dermatitis.Conclusion: The relatively safe adverse effect profile of apremilast and its broad immunomodulatory characteristics may make it a promising option in the future for patients with difficult to treat diseases in dermatology, refractory to first line therapies, but further studies will be necessary to clarify its role.

Computer-assisted learning in critical care: from ENIAC to HAL

Computers are commonly used to serve many functions in today's modern intensive care unit. One of the most intriguing and perhaps most challenging applications of computers has been to attempt to improve medical education. With the introduction of the first computer, medical educators began looking for ways to incorporate their use into the modern curriculum. Prior limitations of cost and complexity of computers have consistently decreased since their introduction, making it increasingly feasible to incorporate computers into medical education. Simultaneously, the capabilities and capacities of computers have increased. Combining the computer with other modern digital technology has allowed the development of more intricate and realistic educational tools. The purpose of this article is to briefly describe the history and use of computers in medical education with special reference to critical care medicine. In addition, we will examine the role of computers in teaching and learning and discuss the types of interaction between the computer user and the computer.

Synthetic peptide analogs to barnacle settlement pheromone

Barnacle pheromone enhances the rate of settlement and metamorphosis of larvae of Balanus amphitrite Darwin. Analogs to the heterogenous pheromone peptides were sought. Settlement assays were used to assess both the pheromone and the potential analogs. The pheromone has a lower threshold of activity at a concentration of 0.2 micrograms BSA protein equivalence l-1. Treatment with carboxypeptidase eliminates biological activity. Series of dipeptides were tested to determine if dipeptides could promote settlement. Combinations of acidic, neutral, and basic amino acids in dipeptides were examined. Specific small peptides can mimic barnacle pheromone. Only peptides with a basic carboxy-terminal amino acid and either a neutral or a basic amino-terminal amino acid enhance settlement. Six peptides were shown to mimic pheromone activity at concentrations comparable to the native molecule. Some peptides were more potent than others. The most effective peptides were L-leucyl-L-arginine and L-histidyl-L-lysine which had a lower threshold of settlement enhancement of 2.0 x 10(-10) M and caused a 130% increase in settlement rate at 2.0 x 10(-8) M. Glycyl-glycyl-L-arginine, glycyl-L-histidyl-L-lysine, L-leucyl-glycyl-L-arginine and L-tyrosyl-L-arginine had thresholds between 2.0 x 10(-8) M and 2.0 x 10(-9) M. Peptide pheromone analogs should be useful in determining the nature and mechanism of barnacle pheromone receptor interactions.