Recent advancements in single-cell metabolic analysis for pharmacological research

Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology.Pharmacological research is being advanced by single-cell metabolic analysis,which offers a technique to identify variations in RNA,proteins,metabolites,and drug molecules in cells.In this review,the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized.High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods,such as droplet microfluidics,microchamber,open microfluidic probe,and digital microfluidics.They are used in tandem with variety of detection techniques,including optical imaging,Raman spectroscopy,electrochemical detection,RNA sequencing,and mass spectrometry,to evaluate single-cell metabolic changes in response to drug administration.The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis.These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway,therapeutic targets discovery,and in vitro disease model evaluation.

Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

Breast cancer resistance protein(BCRP)is an important resistance protein that significantly impacts anticancer drug discovery,treatment,and rehabilitation.Early identification of BCRP substrates is quite a challenging task.This study aims to predict early substrate structure,which can help to optimize anticancer drug development and clinical diagnosis.For this study,a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning(TL)for automatic deep feature(DF)extraction followed by classification with linear discriminant analysis algorithm(TLRNDF-LDA).This study utilized structural fingerprints,which are exploited by DF contrary to conventional molecular descriptors.The proposed in silico model achieved an outstanding accuracy performance of 98.56%on test data compared to other state-of-the-art approaches using standard quality measures.Furthermore,the model’s efficacy is validated via a statistical analysisANOVAtest.It is demonstrated that the developedmodel can be used effectively for early prediction of the substrate structure.The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response,identification of BCRP functions in transport experiments,and prediction of prostate or lung cancer cell lines.

Orotate phosphoribosyl transferase mRNA expression and the response of cholangiocarcinoma to 5-fluorouracil

AIM:To determine whether expression of certain enzymes related to 5-fluorouracil(5-FU)metabolism predicts 5-FU chemosensitivity in cholangiocarcinoma(CCA).METHODS:The histoculture drug response assay(HDRA)was performed using surgically resected CCA tissues.Tumor cell viability was determined morphologically with hematoxylin and eosin-and terminal deoxynucleotide transferase-mediated dUTP nick-end labeling-stained tissues.The mRNA expression of thymidine phosphorylase(TP),orotate phosphoribosyl transferase(OPRT),thymidylate synthase(TS),and dihydropyrimidine dehydrogenase(DPD)was determined with realtime reverse transcriptase-polymerase chain reaction.The levels of gene expression and the sensitivity to 5-FU were evaluated.RESULTS:Twenty-three CCA tissues were obtained from patients who had been diagnosed with intrahepatic CCA and who underwent surgical resection at Srinagarind Hospital,Khon Kaen University from 2007 to 2009.HDRA was used to determine the response of these CCA tissues to 5-FU.Based on the dose-response curve,200μg/mL 5-FU was selected as the test concentration.The percentage of inhibition index at the median point was selected as the cut-off point to differentiate the responding and non-responding tumors to 5-FU.When the relationship between TP,OPRT,TS and DPD mRNA expression levels and the sensitivity of CCA tissues to 5-FU was examined,only OPRT mRNA expression was significantly correlated with the response to 5-FU.The mean expression level of OPRT was significantly higher in the responder group compared to the non-responder group(0.41±0.25 vs 0.22±0.12,P<0.05).CONCLUSION:OPRT mRNA expression may be a useful predictor of 5-FU chemosensitivity of CCA.Whether OPRT mRNA could be used to predict the success of 5-FU chemotherapy in CCA patients requires confirmation in patients.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders.Metabolomics provides a comprehensive understanding of metabolism in biological systems.With advanced analytical techniques,metabolomics exhibits unprecedented significant value in basic drug research,including understanding disease mechanisms,identifying drug targets,and elucidating the mode of action of drugs.More importantly,metabolomics greatly accelerates the drug development process by predicting pharmacokinetics,pharmacodynamics,and drug response.In addition,metabolomics facilitates the exploration of drug repurposing and drug-drug interactions,as well as the development of personalized treatment strategies.Here,we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine.Here,we developed a deep learning framework called TINDL,completely trained on preclinical cancer cell lines(CCLs),to predict the response of cancer patients to different treatments.TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors.Moreover,by making the deep learning black box interpretable,this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model,enabling identification of biomarkers of drug response.Using data from two large databases of CCLs and cancer tumors,we showed that this model can distinguish between sensitive and resistant tumors for 10(out of 14)drugs,outperforming various other machine learning models.In addition,our small interfering RNA(siRNA)knockdown experiments on 10 genes identified by this model for one of the drugs(tamoxifen)confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells,and seven of these genes in T47D cells.Furthermore,genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways.In summary,this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer.The code can be accessed at https://github.com/ddhostallero/tindl.

Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response

Background:Patient-derived organoids and xenografts(PDXs)have emerged as powerful models in functional diag-nostics with high predictive power for anticancer drug response.However,limitations such as engraftment failure and time-consuming for establishing and expanding PDX models followed by testing drug efficacy,and inability to subject to systemic drug administration for ex vivo organoid culture hinder realistic and fast decision-making in selecting the right therapeutics in the clinic.The present study aimed to develop an advanced PDX model,namely MiniPDX,for rapidly testing drug efficacy to strengthen its value in personalized cancer treatment.Methods:We developed a rapid in vivo drug sensitivity assay,OncoVee®MiniPDX,for screening clinically relevant regimens for cancer.In this model,patient-derived tumor cells were arrayed within hollow fiber capsules,implanted subcutaneously into mice and cultured for 7 days.The cellular activity morphology and pharmacokinetics were systematically evaluated.MiniPDX performance(sensitivity,specificity,positive and negative predictive values)was examined using PDX as the reference.Drug responses were examined by tumor cell growth inhibition rate and tumor growth inhibition rate in PDX models and MiniPDX assays respectively.The results from MiniPDX were also used to evaluate its predictive power for clinical outcomes.Results:Morphological and histopathological features of tumor cells within the MiniPDX capsules matched those both in PDX models and in original tumors.Drug responses in the PDX tumor graft assays correlated well with those in the corresponding MiniPDX assays using 26 PDX models generated from patients,including 14 gastric cancer,10 lung cancer and 2 pancreatic cancer.The positive predictive value of MiniPDX was 92%,and the negative predictive value was 81%with a sensitivity of 80%and a specificity of 93%.Through expanding to clinical tumor samples,Min-iPDX assay showed potential of wide clinical application.Conclusions:Fast in vivo MiniPDX assay based on capsule implantation was developed-to assess drug responses of both PDX tumor grafts and clinical cancer specimens.The high correlation between drug responses of paired MiniPDX and PDX tumor graft assay,as well as translational data suggest that MiniPDX assay is an advanced tool for personalized cancer treatment.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action.Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit.With the accumulation of preclinical models and advances in computational approaches of drug response prediction,pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine.In this article,we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines,organoids,and xenografts.Furthermore,we comprehensively review the recent developments of computational methods in drug response prediction,covering network,machine learning,and deep learning technologies and strategies to evaluate immunotherapy response.In the end,we discuss challenges and propose possible solutions for further improvement.

Interpretable prediction of drug-cell line response by triple matrix factorization

Background:One of the challenges in personalized medicine is to determine specific drugs and their dosages for patient individuals who are undergoing a common disease.The technique of cell lines provides a safe approach to capture the drug responses of patient individuals when given specific drugs with varied dosages.However,it is still costly to determine drug responses in cells w.r.t dosages by biological assays.Computational methods provide a promising screening to infer possible drug responses in the cells of patient individuals on a large scale.Nevertheless,existing computational approaches are insufficient to interpret the underlying reason for drug responses.Methods:In this work,we propose an interpretable model for analyzing and predicting drug responses across cell lines.The proposed model bridges drug features(e.g.f chemical structure fingerprints),cell features(e.g.f gene expression profiles),and drug responses across cells(measured by IC50)by a triple matrix factorization(TMF),such that the underlying reason for drug responses in specific cells is possibly interpreted.Results'.The comparison with state-of-the-art computational approaches demonstrates the superiority of our TMF.More importantly,a case study of drug responses in lung-related cell lines shows its interpretable ability to find out highly occurring drug substructures,crucial mutated genes,as well as significant pairs between substructures and mutated genes in terms of drug sensitivity and resistance.Conclusion:TMF is an effective and interpretable approach for predicting cell lines responses to drugs,and can dig out crucial pairs of chemical substructures and genes,which uncovers the underlying reason for drug responses in specific cells.

Author's Reply:Comments on “Vitamin Pharmacogenomics:New Insight into Individua Differences in Diseases and Drug Responses”

Dear Editor, We thank the author for making meaningful comments on our recent article [1]. The SNP 772G 〉 A （rs602662） in exon 2 of the gene encoding fucosyl transferase （FUT2） has been found to be related with the alterations in plasma vitamin B12 levels. GG carriers possessed lower levels of vitamin B12. However, we didn＇t know the mechanism behind this association.

Comments on"Vitamin Pharmacogenomics:New Insight into Individual Differences in Diseases and Drug Responses"

Dear Editor, I would like to offer some comments on the excellent article by Hai-Yan He and colleagues published in Genomics, Proteomics ＆ Bioinformatics on 1st April 2017 [1]. The authors include, in the list of genetic polymorphisms that have an effect on vita- mins, the low concentrations of cellular and plasma vitamin B12 in GG carriers of SNP rs602662 （772 G 〉 A） in the gene encoding fucosyltransferase 2 （FUT2）.

Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

Although anteplleptlc drugs(AEDs)are the most effective treatment for epllepsy,30-40%of patlents with epllepsy would develop drug-efacory eplepsy.An accurate,prellminary predlctlon of the efflcacy of AEDs has great clinical signflcance for patent treatment and prognosts.Some studles have developed statstical models and machine-learning algorithms(MLAS)to predlct the fficacy of AEDs treatment and the progression of disease ater treatment withdrawal,In order to provlde asstance for makng cInlcal decslons In the alm of precse,personalzed treatment The fleld of predcton models with statstical models and MLAs's atracting growing Interest and's developing rapldly.What's more,more and more studles focus on the external valldation of the exlsting model In this revlew,we will glve a brlef overvlew of recent developments In this discipline.

iGMDR:Integrated Pharmacogenetic Resource Guide to Cancer Therapy and Research

Current pharmacogenetic studies have obtained many genetic models that can predict the therapeutic efficacy of anticancer drugs.Although some of these models are of crucial importance and have been used in clinical practice,these very valuable models have not been well adopted into cancer research to promote the development of cancer therapies due to the lack of integration and standards for the existing data of the pharmacogenetic studies.For this purpose,we built a resource investigating genetic model of drug response(iGMDR),which integrates the models from in vitro and in vivo pharmacogenetic studies with different omics data from a variety of technical systems.In this study,we introduced a standardized process for all integrations,and described how users can utilize these models to gain insights into cancer.iGMDR is freely accessible at https://igmdr.modellab.cn.

Pharmacogenetics and cancer management

The science of one’s genetic background and its impact on disease susceptibility and drug response has come of age and firmly established its proper place in the clinic.Its impact is felt more in the treatment of cancer than any other disease area several reasons:critical time,narrow therapeutic index and overlapping toxicity window.We realize that the true potential of pharmacogenetics will be realized when we have been able to integrate other variants like insertion-deletion,copy number variation,etc.,in addition to single nucleotide polymorphism for their collective influence on drug response and toxicity.Technology has rapidly evolved and has become affordable to be used in the clinic once it gets standardized and validated not only in one population but in several major world population-particularly those which are under-represented in human variant database.

Intra-heterogeneity in transcription and chemoresistant property of leukemia-initiating cells in murine Setd2^(-/-) acutemyeloid leukemia

Background:Heterogeneity of leukemia-initiating cells(LICs)is a major obstacle in acute myeloid leukemia(AML)therapy.Accumulated evidence indicates that the coexistence of multiple types of LICs with different pathogenicity in the same individual is a common feature in AML.However,the functional heterogeneity including the drug response of coexistent LICs remains unclear.Therefore,this study aimed to clarify the intra-heterogeneity in LICs that can help predict leukemia behavior and develop more effective treatments.Methods:Spleen cells from the primary Setd2^(-/-)-AML mouse were transplanted into C57BL/6 recipient mice to generate a transplantable model.Flow cytometry was used to analyze the immunophenotype of the leukemic mice.Whole-genome sequencing was conducted to detect secondary hits responsible for leukemia transformation.A serial transplantation assay was used to determine the self-renewal potential of Setd2^(-/-)-AML cells.A limiting-dilution assay was performed to identify the LIC frequency in different subsets of leukemia cells.Bulk and single-cell RNA sequencing were performed to analyze the transcriptional heterogeneity of LICs.Small molecular inhibitor screening and in vivo drug treatment were employed to clarify the difference in drug response between the different subsets of LICs.Results:In this study,we observed an aged Setd2^(-/-)mouse developing AML with co-mutation of Nras^(G12S) and Braf^(K520E).Further investigation identified two types of LICs residing in the c-Kit^(+)B220^(+)Mac-1^(-)and c-Kit^(+)B220^(+)Mac-1^(+)subsets,respectively.In vivo transplantation assay disclosed the heterogeneity in differentiation between the coexistent LICs.Besides,an intrinsic doxorubicinresistant transcriptional signature was uncovered in c-Kit^(+)B220^(+)Mac-1^(+)cells.Indeed,doxorubicin plus cytarabine(DA),the standard chemotherapeutic regimen used in AML treatment,could specifically kill c-Kit^(+)B220^(+)Mac-1^(−)cells,but it hardly affected c-Kit^(+)B220^(+)Mac-1^(+)cells.Transcriptome analysis unveiled a higher activation of RAS downstream signaling pathways in c-Kit^(+)B220^(+)Mac-1^(+)cells than in c-Kit^(+)B220^(+)Mac-1^(-)cells.Combined treatmentwithDAand RAS pathway inhibitors killed both c-Kit^(+)B220^(+)Mac-1^(−)and c-Kit^(+)B220^(+)Mac-1^(+)cells and attenuated disease progression.Conclusions:This study identified two cell subsets enriched for LICs inmurine Setd2^(-/-)-AML and disclosed the transcriptional and functional heterogeneity of LICs,revealing that the coexistence of different types of LICs in thismodel brings about diverse drug response.

Web Resources for Pharmacogenomics

Pharmacogenomics is the study of the impact of genetic variations or genotypes of individuals on their drug response or drug metabolism. Compared to traditional genomics research,pharmacogenomic research is more closely related to clinical practice. Pharmacogenomic discoveries may effectively assist clinicians and healthcare providers in determining the right drugs and proper dose for each patient, which can help avoid side effects or adverse reactions, and improve the drug therapy. Currently, pharmacogenomic approaches have proven their utility when it comes to the use of cardiovascular drugs, antineoplastic drugs, aromatase inhibitors, and agents used for infectious diseases. The rapid innovation in sequencing technology and genome-wide association studies has led to the development of numerous data resources and dramatically changed the landscape of pharmacogenomic research. Here we describe some of these web resources along with their names, web links, main contents, and our ratings.

Molecular mechanisms of enzalutamide resistance in prostate cancer

An estimated 30,000 men in the United States will die of metastatic prostate cancer(PCa)each year due to the development of therapy resistance,most notably resistance to second-generation antiandrogen enzalutamide.The vast majority of PCa is driven by the androgen receptor(AR).Enzalutamide is an AR antagonist,which extends patient survival and is widely used in the clinic for the treatment of castration-resistant prostate cancer(CRPC);however,many patients will have primary or develop acquired resistance and continue to progress.Characterization of the molecular mechanisms of enzalutamide resistance provides insight into potentially efficacious therapies for enzalutamide-resistant CRPC(ER-CRPC).Understanding these mechanisms is critical for the identification of biomarkers predictive of therapy resistance and the development of therapeutic strategies to target ER-CRPC.