Drug-target based cross-sectional analysis of olfactory drug effects

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Olfactory Function and Olfactory Disorders

The sense of smell is important. This became especially clear to patients with infection-related olfactory loss during the SARS-CoV-2 pandemic. We react, for example, to the body odors of other humans. The sense of smell warns us of danger, and it allows us to perceive flavors when eating and drinking. In essence, this means quality of life. Therefore, anosmia must be taken seriously. Although olfactory receptor neurons are characterized by regenerative capacity, anosmia is relatively common with about 5 % of anosmic people in the general population. Olfactory disorders are classified according to their causes (e. g., infections of the upper respiratory tract, traumatic brain injury, chronic rhinosinusitis, age) with the resulting different therapeutic options and prognoses. Thorough history taking is therefore important. A wide variety of tools are available for diagnosis, ranging from short screening tests and detailed multidimensional test procedures to electrophysiological and imaging methods. Thus, quantitative olfactory disorders are easily assessable and traceable. For qualitative olfactory disorders such as parosmia, however, no objectifying diagnostic procedures are currently available. Therapeutic options for olfactory disorders are limited. Nevertheless, there are effective options consisting of olfactory training as well as various additive drug therapies. The consultation and the competent discussion with the patients are of major importance.

International consensus statement on allergy and rhinology: Olfaction

BACKGROUND

The literature regarding clinical olfaction, olfactory loss, and olfactory dysfunction has expanded rapidly over the past two decades, with an exponential rise in the past year. There is substantial variability in the quality of this literature and a need to consolidate and critically review the evidence. It is with that aim that we have gathered experts from around the world to produce this International Consensus on Allergy and Rhinology: Olfaction (ICAR:O).

METHODS

Using previously described methodology, specific topics were developed relating to olfaction. Each topic was assigned a literature review, evidence-based review, or evidence-based review with recommendations format as dictated by available evidence and scope within the ICAR:O document. Following iterative reviews of each topic, the ICAR:O document was integrated and reviewed by all authors for final consensus.

RESULTS

The ICAR:O document reviews nearly 100 separate topics within the realm of olfaction, including diagnosis, epidemiology, disease burden, diagnosis, testing, etiology, treatment, and associated pathologies.

CONCLUSION

This critical review of the existing clinical olfaction literature provides much needed insight and clarity into the evaluation, diagnosis, and treatment of patients with olfactory dysfunction, while also clearly delineating gaps in our knowledge and evidence base that we should investigate further.

Nasal polyp load determines the recovery of olfaction after surgery for chronic rhinosinusitis

BACKGROUND

Chronic rhinosinusitis (CRS) is typically accompanied by impairment of olfaction. Despite of this, until today the efficacy of endonasal sinus surgery (ESS) in terms of olfactory function is still unclear. So far it is known that patients with nasal polyps are most likely to experience post-operative recovery. Within the present study we investigated the sense of smell and other parameters of impairment in CRS before and after ESS in relation to the degree of nasal polyposis, determined with the nasal endoscopic Lildholdt-score.

METHODS

Patients with different degrees of severity of nasal polyposis were included. Olfactory function was assessed for odor thresholds [T], odor discrimination [D] and odor identification [I] and the changes of these parameters were investigated postoperatively.

RESULTS

For 72 patients baseline measures were available and in 47 patients, postoperative changes were described. There was a correlation between olfactory scores and nasal anatomy/polyposis scores (Lildholdt scores, Lund-Mackay CT score), rated nasal health, and nasal quality of life (sinonasal outcome test). Three months after surgery the average TDI-Score improved by 3.1 points with 30% of patients showing significant clinical improvement. Patients with severe polyposis (Lildholdt score of 5 or 6) benefited most in terms of olfaction. Other significant prognostic indicators of a postoperative increase of olfactory scores included younger age, low pre-operative TDI-scores and high CT-scores.

CONCLUSIONS

This study shows that not only the presence of polyps in CRS, but also the degree of nasal polyposis - measured by a grading system - predicts the results in olfactory test results. Additionally, the degree of recovery of olfaction after ESS seems to be most relevant in patients with high polyp scores.

Machine Learning Refutes Loss of Smell as a Risk Indicator of Diabetes Mellitus

Because it is associated with central nervous changes, and olfactory dysfunction has been reported with increased prevalence among persons with diabetes, this study addressed the question of whether the risk of developing diabetes in the next 10 years is reflected in olfactory symptoms. In a cross-sectional study, in 164 individuals seeking medical consulting for possible diabetes, olfactory function was evaluated using a standardized clinical test assessing olfactory threshold, odor discrimination, and odor identification. Metabolomics parameters were assessed via blood concentrations. The individual diabetes risk was quantified according to the validated German version of the “FINDRISK” diabetes risk score. Machine learning algorithms trained with metabolomics patterns predicted low or high diabetes risk with a balanced accuracy of 63–75%. Similarly, olfactory subtest results predicted the olfactory dysfunction category with a balanced accuracy of 85–94%, occasionally reaching 100%. However, olfactory subtest results failed to improve the prediction of diabetes risk based on metabolomics data, and metabolomics data did not improve the prediction of the olfactory dysfunction category based on olfactory subtest results. Results of the present study suggest that olfactory function is not a useful predictor of diabetes.

Predictors of Olfactory Decline in Aging: A Longitudinal Population-Based Study

BACKGROUND

Olfactory dysfunction is common in aging and associated with dementia and mortality. However, longitudinal studies tracking change in olfactory ability are scarce. We sought to identify predictors of interindividual differences in rate of olfactory identification change in aging.

METHOD

Participants were 1780 individuals, without dementia at baseline and with at least 2 olfactory assessments over 12 years of follow-up (mean age = 70.5 years; 61.9% female), from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Odor identification was assessed with the Sniffin' Sticks. We estimated the impact of demographic, health, and genetic factors on rate of olfactory change with linear mixed effect models.

RESULTS

Advancing age, manufacturing profession, history of cerebrovascular disease, higher cardiovascular disease burden, diabetes, slower walking speed, higher number of medications, and the APOE ε4 allele were associated with accelerated odor identification decline (ps < .014). Multi-adjusted analyses showed unique associations of age, diabetes, and ε4 to olfactory decline (ps < .017). In 1531 participants who remained free of dementia (DSM IV criteria) during follow-up, age, cardiovascular disease burden, and diabetes were associated with accelerated decline (ps < .011). Of these, age and diabetes remained statistically significant in the multi-adjusted model (ps < .001).

CONCLUSION

Demographic, vascular, and genetic factors are linked to rate of decline in odor identification in aging. Although some olfactory loss may be an inevitable part of aging, our results highlight the importance of vascular factors for the integrity of the olfactory system, even in the absence of dementia.

Mechanisms Linking Olfactory Impairment and Risk of Mortality

Olfaction is a sense involved in a complex set of tasks, influencing eating behavior, increasing awareness of environmental hazards and affecting social communication. Surprisingly, smell disorders are very frequent, especially in the elderly population. Several recent studies conducted mostly in older subjects have demonstrated a strong association between olfactory impairment and overall mortality risk, with anosmia being even more predictive of 5 years mortality risk than cardiovascular disease. Presently, the underlying pathophysiology linking olfactory impairment to mortality remains unknown and only putative mechanisms are suggested. This review aims to examine the link between olfactory impairment and mortality and to discuss existing ideas on underlying existing mechanisms including, (1) the effect of olfactory loss on nutrition, life-threatening situations and social interactions, (2) associated neurodegenerative diseases, (3) accelerated brain aging, and (4) reflection of general health status being reflected in olfactory function.

Influence of medications on taste and smell

Medications frequently have chemosensory side effects that can adversely affect compliance with medical treatment regimens. Hundreds of drugs have been reported to induce unpleasant tastes and/or odors as well as altered chemosensations when administered alone or in combination with other medications. Some chemosensory complaints are due to the sensory properties of the drug itself such as aversive bitter and metallic tastes. However, most chemosensory side effects of drugs are due to alterations in the transduction pathways, biochemical targets, enzymes, and transporters by the offending medications. Studies of chemosensory perception in medicated older individuals have found that taste and smell loss is greatest for those consuming the largest number of prescription drugs. There are no standard treatments for drug-induced chemosensory disorders because each drug has unique biological effects. However, there are a few treatment options to ameliorate chemosensory alterations including addition of simulated flavors to food to compensate for losses and to override offending tastes and smells.

Olfactory drug effects approached from human-derived data

The complexity of the sense of smell makes adverse olfactory effects of drugs highly likely, which can impact a patient's quality of life. Here, we present a bioinformatics approach that identifies drugs with potential olfactory effects by connecting drug target expression patterns in human olfactory tissue with drug-related information and the underlying molecular drug targets taken from publically available databases. We identified 71 drugs with listed olfactory effects and 147 different targets. Taking the target-based approach further, we found additional drugs with potential olfactory effects, including 152 different substances interacting with genes expressed in the human olfactory bulb. Our proposed bioinformatics approach provides plausible hypotheses about mechanistic drug effects for drug discovery and repurposing and, thus, would be appropriate for use during drug development.