3D bioprinted tumor model: a prompt and convenient platform for overcoming immunotherapy resistance by recapitulating the tumor microenvironment

BACKGROUND

Cancer immunotherapy is receiving worldwide attention for its induction of an anti-tumor response. However, it has had limited efficacy in some patients who acquired resistance. The dynamic and sophisticated complexity of the tumor microenvironment (TME) is the leading contributor to this clinical dilemma. Through recapitulating the physiological features of the TME, 3D bioprinting is a promising research tool for cancer immunotherapy, which preserves in vivo malignant aggressiveness, heterogeneity, and the cell-cell/matrix interactions. It has been reported that application of 3D bioprinting holds potential to address the challenges of immunotherapy resistance and facilitate personalized medication.

CONCLUSIONS AND PERSPECTIVES

In this review, we briefly summarize the contributions of cellular and noncellular components of the TME in the development of immunotherapy resistance, and introduce recent advances in 3D bioprinted tumor models that served as platforms to study the interactions between tumor cells and the TME. By constructing multicellular 3D bioprinted tumor models, cellular and noncellular crosstalk is reproduced between tumor cells, immune cells, fibroblasts, adipocytes, and the extracellular matrix (ECM) within the TME. In the future, by quickly preparing 3D bioprinted tumor models with patient-derived components, information on tumor immunotherapy resistance can be obtained timely for clinical reference. The combined application with tumoroid or other 3D culture technologies will also help to better simulate the complexity and dynamics of tumor microenvironment in vitro. We aim to provide new perspectives for overcoming cancer immunotherapy resistance and inspire multidisciplinary research to improve the clinical application of 3D bioprinting technology.

Herbal compounds as promising therapeutic agents in precision medicine strategies for cancer: A systematic review

BACKGROUND

The field of personalized medicine has gained increasing attention in cancer care, with the aim of tailoring treatment strategies to individual patients for improved outcomes. Herbal medicine, with its long-standing historical use and extensive bioactive compounds, offers a rich source of potential treatments for various diseases, including cancer.

OBJECTIVE

To provide an overview of the current knowledge and evidence associated with incorporating herbal compounds into precision medicine strategies for cancer diseases. Additionally, to explore the general characteristics of the studies included in the analysis, focusing on their key features and trends.

SEARCH STRATEGY

A comprehensive literature search was conducted from multiple online databases, including PubMed, Scopus, Web of Science, and CINAHL-EBSCO. The search strategy was designed to identify studies related to personalized cancer medicine and herbal interventions.

INCLUSION CRITERIA

Publications pertaining to cancer research conducted through in vitro, in vivo, and clinical studies, employing natural products were included in this review.

DATA EXTRACTION AND ANALYSIS

Two review authors independently applied inclusion and inclusion criteria, data extraction, and assessments of methodological quality. The quality assessment and biases of the studies were evaluated based on modified Jadad scales. A detailed quantitative summary of the included studies is presented, providing a comprehensive description of their key features and findings.

RESULTS

A total of 121 studies were included in this review for analysis. Some of them were considered as comprehensive experimental investigations both in vitro and in vivo. The majority (n = 85) of the studies included in this review were conducted in vitro, with 44 of them specifically investigating the effects of herbal medicine on animal models. Additionally, 7 articles with a combined sample size of 31,271 patients, examined the impact of herbal medicine in clinical settings.

CONCLUSION

Personalized medication can optimize the use of herbal medicine in cancer treatment by considering individual patient factors such as genetics, medical history, and other treatments. Additionally, active phytochemicals found in herbs have shown potential for inhibiting cancer cell growth and inducing apoptosis, making them a promising area of research in preclinical and clinical investigations. Please cite this article as: Tayeb BA, Kusuma IY, Osman AAM, Minorics R. Herbal compounds as promising therapeutic agents in precision medicine strategies for cancer: A systematic review. J Integr Med. 2024; 22(2): 137-162.

Mechanisms and Predictive Biomarkers of Allergen Immunotherapy in the Clinic

Allergen immunotherapy (AIT) remains to be the only disease-modifying treatment for IgE-mediated allergic diseases such as allergic rhinitis. It can provide long-term clinical benefits when given for 3 years or longer. Mechanisms of immune tolerance induction by AIT are underscored by the modulation of several compartments within the immune system. These include repair of disruption in epithelial barrier integrity, modulation of the innate immune compartment that includes regulatory dendritic cells and innate lymphoid cells, and adaptive immune compartments such as induction of regulatory T and B cells. Altogether, these are also associated with the dampening of allergen-specific TH2 and T follicular helper cell responses and subsequent generation of blocking antibodies. Although AIT is effective in modifying the immune response, there is a lack of validated and clinically relevant biomarkers that can be used to monitor desensitization, efficacy, and the likelihood of response, all of which can contribute to accelerating personalized medication and increasing patient care. Candidate biomarkers comprise humoral, cellular, metabolic, and in vivo biomarkers; however, these are primarily studied in small trials and require further validation. In this review, we evaluate the current candidates of biomarkers of AIT and how we can implement changes in future studies to help us identify clinically relevant biomarkers of safety, compliance, and efficacy.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

A multicenter clinical study: personalized medication for advanced gastrointestinal carcinomas with the guidance of patient-derived tumor xenograft (PDTX)

BACKGROUND

Establish patient-derived tumor xenograft (PDTX) from advanced GICs and assess the clinical value and applicability of PDTX for the treatment of advanced gastrointestinal cancers.

METHODS

Patients with advanced GICs were enrolled in a registered multi-center clinical study (ChiCTR-OOC-17012731). The performance of PDTX was evaluated by analyzing factors that affect the engraftment rate, comparing the histological consistency between primary tumors and tumorgrafts, examining the concordance between the drug effectiveness in PDTXs and clinical responses, and identifying genetic variants and other factors associated with prognosis.

RESULTS

Thirty-three patients were enrolled in the study with the engraftment rate of 75.8% (25/33). The success of engraftment was independent of age, cancer types, pathological stages of tumors, and particularly sampling methods. Tumorgrafts retained the same histopathological characteristics as primary tumors. Forty-nine regimens involving 28 drugs were tested in seventeen tumorgrafts. The median time for drug testing was 134.5 days. Follow-up information was obtained about 10 regimens from 9 patients. The concordance of drug effectiveness between PDTXs and clinical responses was 100%. The tumor mutation burden (TMB) was correlated with the effectiveness of single drug regimens, while the outgrowth time of tumorgrafts was associated with the effectiveness of combined regimens.

CONCLUSION

The engraftment rate in advanced GICs was higher than that of other cancers and meets the acceptable standard for applying personalized therapeutic strategies. Tumorgrafts from PDTX kept attributes of the primary tumor. Predictions from PDTX modeling closely agreed with clinical drug responses. PDTX may already be clinically applicable for personalized medication in advanced GICs.

Combination of CYP2C19 genotype with non-genetic factors evoking phenoconversion improves phenotype prediction

BACKGROUND

CYP2C19 is an important drug-metabolizing enzyme, responsible for metabolism of approximately 10% of the drugs on the market. Large inter-individual differences exist in metabolic activities, which are primarily attributed to genetic polymorphism of CYP2C19 gene. Conflicting results have been published about the role of CYP2C19 polymorphisms in metabolism of CYP2C19 substrates and clinical outcomes; thus, we aimed to investigate CYP2C19 genotype-phenotype associations, and we sought to elicit potential causes of discrepancies in the genotype-based prediction by incorporating the liver donors' demographic data, drug administration events and pathological conditions.

METHODS

(S)-Mephenytoin was used to assess CYP2C19 activities in human liver microsomes derived from 114 Hungarian organ donors. CYP2C19 genotype was determined by SNP genotyping for CYP2C19*2, CYP2C19*3, CYP2C19*4 and CYP2C19*17 variants, and CYP2C19 mRNA levels were measured by qPCR method. Clinical data of the donors were considered in the genotype-based phenotype prediction.

RESULTS

CYP2C19 phenotype of 40% of the donors was well-predicted from the genotype data, whereas the phenotype of 13% was underestimated displaying higher activity, and of 47% was overestimated displaying lower activity than predicted from CYP2C19 genotype. Among the donors with overestimated phenotype, one was treated with CYP2C19 substrate/inhibitor, 9 were on amoxicillin-clavulanic acid therapy, 7 were chronic alcohol consumers and 9 had disease with inflammatory processes.

CONCLUSIONS

CYP2C19 genotype only partially determines the CYP2C19 phenotypic appearance; co-medication, diseases with inflammatory processes and aspecific factors, such as chronic alcohol consumption and amoxicillin-clavulanic acid therapy (or any drug therapy resulting in liver injury) seem to be potential phenotype-modifying factors.

Regulation of drug metabolism and toxicity by multiple factors of genetics, epigenetics, lncRNAs, gut microbiota, and diseases: a meeting report of the 21st International Symposium on Microsomes and Drug Oxidations (MDO)

Variations in drug metabolism may alter drug efficacy and cause toxicity; better understanding of the mechanisms and risks shall help to practice precision medicine. At the 21^st International Symposium on Microsomes and Drug Oxidations held in Davis, California, USA, in October 2-6, 2016, a number of speakers reported some new findings and ongoing studies on the regulation mechanisms behind variable drug metabolism and toxicity, and discussed potential implications to personalized medications. A considerably insightful overview was provided on genetic and epigenetic regulation of gene expression involved in drug absorption, distribution, metabolism, and excretion (ADME) and drug response. Altered drug metabolism and disposition as well as molecular mechanisms among diseased and special populations were presented. In addition, the roles of gut microbiota in drug metabolism and toxicology as well as long non-coding RNAs in liver functions and diseases were discussed. These findings may offer new insights into improved understanding of ADME regulatory mechanisms and advance drug metabolism research.

Variation in the inhibitory potency of terbinafine among genetic variants of CYP2D6

Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic enzyme that is involved in the metabolism of many drugs. Terbinafine (TER) is a CYP2D6 inhibitor and causes persistent drug interactions in the clinical setting; however, its inhibitory mechanism and the differences in its inhibitory potency among genetic variants of CYP2D6 remain to be investigated. This study aimed to investigate the inhibitory mechanism of TER and the differences in its inhibitory potency among three CYP2D6 variants, CYP2D6.1, CYP2D6.2, and CYP2D6.10. In a competitive inhibition study, the metabolic activity of the CYP2D6 was assessed based on their demethylation of dextromethorphan in the presence or absence of TER, and the time-dependency of the inhibitory effects were examined by preincubating the enzymes with TER. TER had weaker inhibitory effects on CYP2D6.2 and CYP2D6.10 than on CYP2D6.1; i.e., TER exhibited Ki values (the concentration of inhibitor that results in half-maximal inhibition) of 0.0525, 0.355, and 1.85 μM for CYP2D6.1, CYP2D6.2, and CYP2D6.10, respectively. The inhibitory effects of TER were not time-dependent. Since TER's Ki value for CYP2D6.10 was 35.2-fold higher than its Ki value for CYP2D6.1, the CYP2D6 genotype of subjects should be taken into account when estimating the severity of drug interactions involving TER.