Brief Bioinform. 2021 Aug 11:bbab294. doi: 10.1093/bib/bbab294. Online ahead of print.

ABSTRACT

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

PMID:34382071 | DOI:10.1093/bib/bbab294

Front Pharmacol. 2021 Jul 26;12:713178. doi: 10.3389/fphar.2021.713178. eCollection 2021.

ABSTRACT

The human leukocyte antigen haplotypes HLA-B*15:02 and HLA-A*31:01 have been linked to life-threatening adverse drug reactions to the anticonvulsants carbamazepine and oxcarbazepine. Identification of these haplotypes via pharmacogenetic techniques facilitates implementation of precision medicine to prevent such reactions. Using reference samples from diverse ancestral origins, we investigated the test analytical validity (i.e., ability to detect whether or not the haplotypes were present or absent) of TaqMan assays for single nucleotide variants previously identified as potentially being able to "tag" these haplotypes. A TaqMan custom assay for rs10484555 and an inventoried assay for rs17179220 and were able to identify with 100% sensitivity and 100% specificity HLA-B*15:02 and HLA-A*31:01 respectively. A custom assay for rs144012689 that takes into account a neighboring single nucleotide variant with manual calling was also able to identify HLA-B*15:02 with 100% sensitivity and 100% specificity. A custom assay for rs106235 identified HLA-A*31:01 with 100% sensitivity and 95% specificity. The slight reduction in specificity for the latter was owing to another haplotype (HLA-A*33:03) also being detected. While any positive call using the rs106235 assay could therefore be further investigated, as the presence of the HLA-A*31:01 haplotype confers adverse drug reaction risk, the absence of false negatives (indexed by sensitivity) is more important than false positives. In summary, we present validated TaqMan assay methodology for efficient detection of HLA haplotypes HLA-B*15:02 and HLA-A*31:01. Our data are relevant for other genotyping technologies that identify, or have the potential to identify, these haplotypes using single nucleotide variants.

PMID:34381365 | PMC:PMC8350439 | DOI:10.3389/fphar.2021.713178

Pharmacogenomics J. 2021 Aug 11. doi: 10.1038/s41397-021-00250-8. Online ahead of print.

ABSTRACT

Polymorphisms in genes associated with opioid signaling and dopamine reuptake and inactivation may moderate naltrexone efficacy in Alcohol Use Disorder (AUD), but the effects of epigenetic modification of these genes on naltrexone response are largely unexplored. This study tested interactions between methylation in the μ-opioid receptor (OPRM1), dopamine transporter (SLC6A3), and catechol-O-methyltransferase (COMT) genes as predictors of naltrexone effects on heavy drinking in a 16-week randomized, placebo-controlled trial among 145 treatment-seeking AUD patients. OPRM1 methylation interacted with both SLC6A3 and COMT methylation to moderate naltrexone efficacy, such that naltrexone-treated individuals with lower methylation of the OPRM1 promoter and the SLC6A3 promoter (p = 0.006), COMT promoter (p = 0.005), or SLC6A3 3' untranslated region (p = 0.004), relative to placebo and to those with higher OPRM1 and SLC6A3 or COMT methylation, had significantly fewer heavy drinking days. Epigenetic modification of opioid- and dopamine-related genes may represent a novel pharmacoepigenetic predictor of naltrexone efficacy in AUD.

PMID:34381173 | DOI:10.1038/s41397-021-00250-8

Biomed Res. 2021;42(4):121-127. doi: 10.2220/biomedres.42.121.

ABSTRACT

Fluoropyrimidines such as 5-fluorouracil (5-FU) are well known to have drug-drug interactions with anticoagulant medications such as warfarin. This study investigated the mRNA expression of pharmacokinetic (PK)-related genes in response to 5-FU using the hepatocarcinoma cell lines after examining relevant gene expression via RNA sequencing. We used HepaRG cells for 5-FU treatment analysis because these cells displayed PK-related gene expression. 5-FU exposure significantly reduced cytochrome P450 3A4 (CYP3A4) mRNA expression. Additionally, the mRNA expression of nuclear receptor subfamily 1 group I member 2 (also known as pregnane X receptor), a nuclear receptor transcription factor that promotes the expression of many CYP genes, was also decreased in HepaRG cells following 5-FU treatment. The mRNA expressions of the CYP2B6 and ATP-binding cassette transporter genes were decreased after 5-FU treatment. This study revealed that 5-FU treatment reduced PK-related gene expression in HepaRG cells. These findings should be useful for further drug-drug interaction research.

PMID:34380920 | DOI:10.2220/biomedres.42.121

Comput Struct Biotechnol J. 2021 Jul 8;19:4003-4017. doi: 10.1016/j.csbj.2021.07.003. eCollection 2021.

ABSTRACT

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

PMID:34377366 | PMC:PMC8321893 | DOI:10.1016/j.csbj.2021.07.003

Pharmacogenomics J. 2021 Aug 10. doi: 10.1038/s41397-021-00248-2. Online ahead of print.

ABSTRACT

Variable responses to medications complicates perioperative care. As a potential solution, we evaluated and synthesized pharmacogenomic evidence that may inform anesthesia and pain prescribing to identify clinically actionable drug/gene pairs. Clinical decision-support (CDS) summaries were developed and were evaluated using Appraisal of Guidelines for Research and Evaluation (AGREE) II. We found that 93/180 (51%) of commonly-used perioperative medications had some published pharmacogenomic information, with 18 having actionable evidence: celecoxib/diclofenac/flurbiprofen/ibuprofen/piroxicam/CYP2C9, codeine/oxycodone/tramadol CYP2D6, desflurane/enflurane/halothane/isoflurane/sevoflurane/succinylcholine/RYR1/CACNA1S, diazepam/CYP2C19, phenytoin/CYP2C9, succinylcholine/mivacurium/BCHE, and morphine/OPRM1. Novel CDS summaries were developed for these 18 medications. AGREE II mean ± standard deviation scores were high for Scope and Purpose (95.0 ± 2.8), Rigor of Development (93.2 ± 2.8), Clarity of Presentation (87.3 ± 3.0), and Applicability (86.5 ± 3.7) (maximum score = 100). Overall mean guideline quality score was 6.7 ± 0.2 (maximum score = 7). All summaries were recommended for clinical implementation. A critical mass of pharmacogenomic evidence exists for select medications commonly used in the perioperative setting, warranting prospective examination for clinical utility.

PMID:34376788 | DOI:10.1038/s41397-021-00248-2

Am J Respir Crit Care Med. 2021 Aug 10. doi: 10.1164/rccm.202103-0564OC. Online ahead of print.

ABSTRACT

RATIONALE: Standardized dosing of anti-tubercular drugs contributes to a substantial incidence of toxicities, inadequate treatment response, and relapse, in part due to variable drug levels achieved. Single nucleotide polymorphisms (SNPs) in the N-acetyltransferase-2 (NAT2) gene explain the majority of interindividual pharmacokinetic variability of isoniazid (INH). However, an obstacle to implementing pharmacogenomic-guided dosing is the lack of a point-of-care assay.

OBJECTIVES: To develop and test a NAT2 classification algorithm, validate its performance in predicting isoniazid clearance, and develop a prototype pharmacogenomic assay.

METHODS: We trained random forest models to predict NAT2 acetylation genotype from unphased SNP data using a global collection of 8,561 phased genomes. We enrolled 48 pulmonary TB patients, performed sparse pharmacokinetic sampling, and tested the acetylator prediction algorithm accuracy against estimated INH clearance. We then developed a cartridge-based multiplex qPCR assay on the GeneXpert platform and assessed its analytical sensitivity on whole blood samples from healthy individuals.

MEASUREMENTS AND MAIN RESULTS: With a 5-SNP model trained on two-thirds of the data (n=5,738), out-of-sample acetylation genotype prediction accuracy on the remaining third (n=2,823) was 100%. Among the 48 TB patients, predicted acetylator types were: 27 (56.2%) slow, 16 (33.3%) intermediate and 5 (10.4%) rapid. INH clearance rates were lowest in predicted slow acetylators (median 14.5 L/hr), moderate in intermediate acetylators (median 40.3 L/hr) and highest in fast acetylators (median 53.0 L/hr). The cartridge-based assay accurately detected all allele patterns directly from 25 ul of whole blood.

CONCLUSIONS: An automated pharmacogenomic assay on a platform widely used globally for tuberculosis diagnosis could enable personalized dosing of isoniazid.

PMID:34375564 | DOI:10.1164/rccm.202103-0564OC

J Physiol Pharmacol. 2021 Apr;72(2). doi: 10.26402/jpp.2021.2.08. Epub 2021 Aug 6.

ABSTRACT

The Asian ginseng root (Panax ginseng C.A. Meyer) is a very commonly used herbal medicine worldwide. Ginseng fruit, including the berry (or pulp) and seed, is also valuable for several health conditions including immunostimulation and cancer chemoprevention. In this study, the anticancer and anti-proliferative effects of the extracts of ginseng berry and seed were evaluated. The ginsenosides in the ginseng berry concentrate (GBC) and ginseng seed extract (GSE) were analyzed. We then evaluated their anti-colorectal cancer potentials, including antiproliferation, cell cycle arrest, and apoptotic induction. Further investigation consisted of the berry's adaptive immune responses, such as the actions on the differentiation of T helper cells Treg, Th1, and Th17. The major constituents in GBC were ginsenosides Re and Rd, which can be compared to those in the root. The GBC significantly inhibited colon cancer cell growth, and its anti-proliferative effect involved mechanisms including G2/M cell cycle arrest via upregulation of cyclin A and induction of apoptosis via regulation of apoptotic related gene expressions. GBC also downregulated the expressions of pro-inflammatory cytokine genes. For the adaptive immune responses, GBC did not influence Th1 and Treg cell differentiation but significantly inhibited Th17 cell differentiation and thus regulated the balance of Th17/Treg for adaptive immunity. Although no ginsenoside was detected in the GSE, interestingly, it obviously enhanced colon cancer cell proliferation with the underlined details to be determined. Our results suggested that GBC is a promising dietary supplement for cancer chemoprevention and immunomodulation.

PMID:34374659 | DOI:10.26402/jpp.2021.2.08

Sci Rep. 2021 Aug 9;11(1):16158. doi: 10.1038/s41598-021-95618-y.

ABSTRACT

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

PMID:34373545 | DOI:10.1038/s41598-021-95618-y

Cancer Discov. 2021 Aug 9:candisc.0094.2021. doi: 10.1158/2159-8290.CD-21-0094. Online ahead of print.

ABSTRACT

INFORM is a prospective, multi-national registry gathering clinical and molecular data of relapsed, progressive or high-risk pediatric cancer patients. This report describes long-term follow-up of 519 patients in whom molecular alterations were evaluated according to a pre-defined 7-scale target prioritization algorithm. Mean turnaround time from sample receipt to report was 25.4 days. The highest target priority level was observed in 42 patients (8.1%). Of these, twenty patients received matched targeted treatment with a median PFS of 204 days (95% CI 99 - N.A.), compared with 117 days (95% CI 106 - 143; P=0.011) in all other patients. The respective molecular targets were shown to be predictive for matched treatment response and not prognostic surrogates for improved outcome. Hereditary cancer predisposition syndromes were identified in 7.5% of patients, half of which were newly identified through the study. Integrated molecular analyses resulted in a change or refinement of diagnoses in 8.2% of cases.

PMID:34373263 | DOI:10.1158/2159-8290.CD-21-0094

Hum Genomics. 2021 Aug 9;15(1):51. doi: 10.1186/s40246-021-00352-1.

ABSTRACT

BACKGROUND: The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility.

METHODS: This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation.

RESULTS: All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function."

CONCLUSION: Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

PMID:34372920 | DOI:10.1186/s40246-021-00352-1

Diabetes Metab Syndr. 2021 Aug 3;15(5):102239. doi: 10.1016/j.dsx.2021.102239. Online ahead of print.

ABSTRACT

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a prevalent disease with incidences increasing globally at a rapid rate. The goal of T2DM treatment is to control glucose levels and prevent the aggravation of glycemic symptoms.

TREATMENT OPTIONS: T2DM regimen include metformin as the first-line, with sulfonylurea, thiazolidinedione (TZD), GLP-1, DPP4I, and SGLT2 inhibitor as the second-line treatment options. However, even with a multitude of choices, patient-to-patient variability due to pharmacogenomic differences still prevail.

CONCLUSION: This review aims to discuss the responses of the major T2DM medications influenced by pharmacogenomics and investigate improved personalized therapy for T2DM patients.

PMID:34371302 | DOI:10.1016/j.dsx.2021.102239

Crit Rev Oncol Hematol. 2021 Aug 6:103436. doi: 10.1016/j.critrevonc.2021.103436. Online ahead of print.

ABSTRACT

The personalized medicine is in a rapidly evolving scenario. The identification of actionable mutations is revolutionizing the therapeutic landscape of tumors. The morphological and histological tumor features are enriched by the extensive genomic profiling, and the first tumor-agnostic drugs have been approved regardless of tumor histology, guided by predictive and druggable genetic alterations. This new paradigm of "mutational oncology", presents a great potential to change the oncologic therapeutic scenario, but also some critical aspects need to be underlined. A process governance is mandatory to ensure the genomic testing accuracy and homogeneity, the economic sustainability, and the regulatory issues, ultimately granting the possibility of translating this model in the "real world". In this position paper, based on experts' opinion, the AIOM-SIAPEC-IAP-SIBIOC-SIF Italian Scientific Societies revised the new agnostic biomarkers, the diagnostic technologies available, the current availability of agnostic drugs and their present indication.

PMID:34371157 | DOI:10.1016/j.critrevonc.2021.103436

J Mol Med (Berl). 2021 Aug 9. doi: 10.1007/s00109-021-02105-y. Online ahead of print.

ABSTRACT

Adenosine triphosphate-binding cassette subfamily B member 1 (ABCB1), also known as permeability glycoprotein, multidrug-resistant protein 1, or cluster of differentiation 243 (CD243), is a crucial protein for purging foreign substances from cells. The functions of ABCB1 have been investigated extensively for their roles in cancer, stem cells, and drug resistance. Abundant pharmacogenetic studies have been conducted on ABCB1 and its association with treatment responsiveness to various agents, particularly chemotherapeutic and immunomodulatory agents. However, its functions in the skin and implications on dermatotherapeutics are far less reported. In this article, we reviewed the roles of ABCB1 in dermatology. ABCB1 is expressed in the skin and its appendages during drug delivery and transport. It is associated with treatment responsiveness to various agents, including topical steroids, methotrexate, cyclosporine, azathioprine, antihistamines, antifungal agents, colchicine, tacrolimus, ivermectin, tetracycline, retinoid acids, and biologic agents. Moreover, genetic variation in ABCB1 is associated with the pathogenesis of several dermatoses, including psoriasis, atopic dermatitis, melanoma, bullous pemphigoid, Behçet disease, and lichen planus. Further investigation is warranted to elucidate the roles of ABCB1 in dermatology and the possibility of enhancing therapeutic efficacy through ABCB1 manipulation.

PMID:34370042 | DOI:10.1007/s00109-021-02105-y

Am J Health Syst Pharm. 2021 Aug 9:zxab311. doi: 10.1093/ajhp/zxab311. Online ahead of print.

NO ABSTRACT

PMID:34369999 | DOI:10.1093/ajhp/zxab311

Pharmacogenet Genomics. 2021 Aug 6. doi: 10.1097/FPC.0000000000000448. Online ahead of print.

ABSTRACT

OBJECTIVE: In AIDS Clinical Trials Group study A5338, concomitant rifampicin, isoniazid, and efavirenz was associated with more rapid plasma medroxyprogesterone acetate (MPA) clearance compared to historical controls without tuberculosis or HIV therapy. We characterized the pharmacogenetics of this interaction.

METHODS: In A5338, women receiving efavirenz-based HIV therapy and rifampicin plus isoniazid for tuberculosis underwent pharmacokinetic evaluations over 12 weeks following a 150-mg intramuscular injection of depot MPA. Data were interpreted with nonlinear mixed-effects modelling. Associations between individual pharmacokinetic parameters and polymorphisms relevant to rifampicin, isoniazid, efavirenz, and MPA were assessed.

RESULTS: Of 62 A5338 participants in four African countries, 44 were evaluable for pharmacokinetic associations, with 17 CYP2B6 normal, 21 intermediate, and 6 poor metabolizers, and 5 NAT2 rapid, 20 intermediate, and 19 slow acetylators. There were no associations between either CYP2B6 or NAT2 genotype and MPA Cmin at week 12, apparent clearance, Cmax, area under the concentration-time curve (AUC) or half-life, or unexplained interindividual variability in clearance, and uptake rate constant or mean transit time of the slow-release fraction (P > 0.05 for each). In exploratory analyses, none of 28 polymorphisms in 14 genes were consistently associated with MPA pharmacokinetic parameters, and none withstood correction for multiple testing.

CONCLUSIONS: Study A5338 suggested that more frequent depot MPA dosing may be appropriate for women receiving rifampicin, isoniazid, and efavirenz. The present results suggest that knowledge of CYP2B6 metabolizer or NAT2 acetylator status does not inform individualized DMPA dosing in this setting.

PMID:34369424 | DOI:10.1097/FPC.0000000000000448

Brief Bioinform. 2021 Aug 6:bbab312. doi: 10.1093/bib/bbab312. Online ahead of print.

ABSTRACT

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

PMID:34368843 | DOI:10.1093/bib/bbab312

Front Public Health. 2021 Jul 23;9:704115. doi: 10.3389/fpubh.2021.704115. eCollection 2021.

NO ABSTRACT

PMID:34368066 | PMC:PMC8342846 | DOI:10.3389/fpubh.2021.704115

Front Pharmacol. 2021 Jul 23;12:542342. doi: 10.3389/fphar.2021.542342. eCollection 2021.

ABSTRACT

Genetic variability was linked with individual responses to treatment and susceptibility to malaria by Plasmodium vivax. Polymorphisms in the CYP2D6 gene may modulate enzyme level and activity, thereby affecting individual responses to pharmacological treatment. The aim of the study was to investigate whether or not CYP2D6 single nucleotide polymorphisms rs1065852, rs38920-97, rs16947 and rs28371725 are unequally distributed in malaria by Plasmodium vivax individuals from the Brazilian Amazon region. The blood samples were collected from 220 unrelated Plasmodium vivax patients from five different endemic areas. Genotyping was performed using SNaPshot® and real-time polymerase chain reaction methods. In all five areas, the rs1065852 (CYP2D6*10, C.100C > T), rs3892097 (CYP2D6*4, 1846C > T) and rs16947 (CYP2D6*2, C.2850G > A), as a homozygous genotype, showed the lowest frequencies. The rs28371725 (CYP2D6*41, 2988G > A) homozygous genotype was not detected, while the allele A was found in a single patient from Macapá region. No deviations from Hardy-Weinberg equilibrium were found, although a borderline p-value was observed (p = 0.048) for the SNP rs3892097 in Goianésia do Pará, Pará state. No significant associations were detected in these frequencies among the five studied areas. For the SNP rs3892097, a higher frequency was observed for the C/T heterozygous genotype in the Plácido de Castro and Macapá, Acre and Amapá states, respectively. The distribution of the CYP2D6 alleles investigated in the different areas of the Brazilian Amazon is not homogeneous. Further investigations are necessary in order to determine which alleles might be informative to assure optimal drug dosing recommendations based on experimental pharmacogenetics.

PMID:34366834 | PMC:PMC8343396 | DOI:10.3389/fphar.2021.542342

J Allergy Clin Immunol Pract. 2021 Aug;9(8):2958-2966. doi: 10.1016/j.jaip.2021.04.006.

ABSTRACT

Drug hypersensitivity reactions (DHR) are heterogeneous in their pathomechanisms, clinical presentation, severity, and outcomes. Novel DHR mechanisms, phenotypes, and endotypes have been described. The key to prevention from further exposure to the culprit drugs involves correct identification of the putative drug through a combination of in vitro and/or in vivo tests, accurate drug allergy labeling and reporting, and electronic decision support systems within electronic medical records to prevent future accidental prescribing. Prescreening and premedication, the focus of this review, may be a useful adjunct to preventive measures in certain situations. After an index immediate drug hypersensitivity reaction, prescreening may be useful in perioperative anaphylaxis, and iodinated (ICM) and gadolinium-based contrast media (GCM) where the culprit and potential alternative agents are skin tested. In certain nonimmediate DHR, pharmacogenomic prescreening may be used before prescribing high-risk drugs (eg, carbamazepine and allopurinol) where specific human-leukocyte antigen genotypes are associated with severe cutaneous adverse reactions. Premedication with antihistamine and systemic corticosteroids is another therapeutic strategy to prevent infusion reactions for certain biologicals and chemotherapeutic agents, in cases of perioperative anaphylaxis, ICM and GCM DHR, and clonal mast cell disorders. Rapid drug desensitization may also be used to induce temporary tolerance in situations where there are limited alternative drugs.

PMID:34366094 | DOI:10.1016/j.jaip.2021.04.006