Targeted therapy for multiple myeloma: an overview on CD138-based strategies.

Multiple myeloma (MM) is an incurable hematological disease characterized by the uncontrolled growth of plasma cells primarily in the bone marrow. Although its treatment consists of the administration of combined therapy regimens mainly based on immunomodulators and proteosome inhibitors, MM remains incurable, and most patients suffer from relapsed/refractory disease with poor prognosis and survival. The robust results achieved by immunotherapy targeting MM-associated antigens CD38 and CD319 (also known as SLAMF7) have drawn attention to the development of new immune-based strategies and different innovative compounds in the treatment of MM, including new monoclonal antibodies, antibody-drug conjugates, recombinant proteins, synthetic peptides, and adaptive cellular therapies. In this context, Syndecan1 (CD138 or SDC1), a transmembrane heparan sulfate proteoglycan that is upregulated in malignant plasma cells, has gained increasing attention in the panorama of MM target antigens, since its key role in MM tumorigenesis, progression and aggressiveness has been largely reported. Here, our aim is to provide an overview of the most important aspects of MM disease and to investigate the molecular functions of CD138 in physiologic and malignant cell states. In addition, we will shed light on the CD138-based therapeutic approaches currently being tested in preclinical and/or clinical phases in MM and discuss their properties, mechanisms of action and clinical applications.

Therapeutic Monitoring of Palbociclib, Ribociclib, Abemaciclib, M2, M20, and Letrozole in Human Plasma: A Novel LC-MS/MS Method.

BACKGROUND: Therapeutic drug monitoring (TDM) using cyclin-dependent kinase inhibitors (CDK4/6is) is a novel approach for optimizing treatment outcomes. Currently, palbociclib, ribociclib, and abemaciclib are the available CDK4/6is and are primarily coadministered with letrozole. This study aimed to develop and validate an LC-MS/MS method for the simultaneous analysis of CDK4/6is, 2 active metabolites of abemaciclib (M2 and M20), and letrozole in human plasma for use in TDM studies. METHODS: Sample pretreatment comprised protein precipitation with methanol and dilution of the supernatant with an aqueous mobile phase. Chromatographic separation was achieved using a reversed-phase XBridge BEH C18 column (2.5 µm, 3.0 × 75 mm XP), with methanol serving as the organic mobile phase and pyrrolidine-pyrrolidinium formate (0.005:0.005 mol/L) buffer (pH 11.3) as the aqueous mobile phase. A triple quadrupole mass spectrometer was used for the detection, with the ESI source switched from negative to positive ionization mode and the acquisition performed in multiple reaction monitoring mode. RESULTS: The complete validation procedure was successfully performed in accordance with the latest regulatory guidelines. The following analytical ranges (ng/mL) were established for the tested compounds: 6-300, palbociclib and letrozole; 120-6000, ribociclib; 40-800, abemaciclib; and 20-400, M2 and M20. All results met the acceptance criteria for linearity, accuracy, precision, selectivity, sensitivity, matrix effects, and carryover. A total of 85 patient samples were analyzed, and all measured concentrations were within the validated ranges. The percent difference for the reanalyzed samples ranged from -11.2% to 7.0%. CONCLUSIONS: A simple and robust LC-MS/MS method was successfully validated for the simultaneous quantification of CDK4/6is, M2, M20, and letrozole in human plasma. The assay was found to be suitable for measuring steady-state trough concentrations of the analytes in patient samples.

The burden of rare variants in DPYS gene is a novel predictor of the risk of developing severe fluoropyrimidine-related toxicity.

BACKGROUND: Despite a growing number of publications highlighting the potential impact on the therapy outcome, rare genetic variants (minor allele frequency < 1%) in genes associated to drug adsorption, distribution, metabolism, and elimination are poorly studied. Previously, rare germline DPYD missense variants were shown to identify a subset of fluoropyrimidine-treated patients at high risk for severe toxicity. Here, we investigate the impact of rare genetic variants in a panel of 54 other fluoropyrimidine-related genes on the risk of severe toxicity. METHODS: The coding sequence and untranslated regions of 54 genes related to fluoropyrimidine pharmacokinetics/pharmacodynamics were analyzed by next-generation sequencing in 120 patients developing grade 3-5 toxicity (NCI-CTC vs3.0) and 104 matched controls. Sequence Kernel Association Test (SKAT) analysis was used to select genes with a burden of genetic variants significantly associated with risk of severe toxicity. The statistical association of common and rare genetic variants in selected genes was further investigated. The functional impact of genetic variants was assessed using two different in silico prediction tools (Predict2SNP; ADME Prediction Framework). RESULTS: SKAT analysis highlighted DPYS and PPARD as genes with a genetic mutational burden significantly associated with risk of severe fluoropyrimidine-related toxicity (Bonferroni adjusted P = 0.024 and P = 0.039, respectively). Looking more closely at allele frequency, the burden of rare DPYS variants was significantly higher in patients with toxicity compared with controls (P = 0.047, Mann-Whitney test). Carrying at least one rare DPYS variant was associated with an approximately fourfold higher risk of severe cumulative (OR = 4.08, P = 0.030) and acute (OR = 4.21, P = 0.082) toxicity. The burden of PPARD rare genetic variants was not significantly related to toxicity. Some common variants with predictive value in DPYS and PPARD were also identified: DPYS rs143004875-T and PPARD rs2016520-T variants predicted an increased risk of severe cumulative (P = 0.002 and P = 0.001, respectively) and acute (P = 0.005 and P = 0.0001, respectively) toxicity. CONCLUSION: This work demonstrated that the rare mutational burden of DPYS, a gene strictly cooperating with DPYD in the catabolic pathway of fluoropyrimidines, is a promising pharmacogenetic marker for precision dosing of fluoropyrimidines. Additionally, some common genetic polymorphisms in DPYS and PPARD were identified as promising predictive markers that warrant further investigation.

A novel complement-fixing IgM antibody targeting GPC1 as a useful immunotherapeutic strategy for the treatment of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers with a very low survival rate at 5 years. The use of chemotherapeutic agents results in only modest prolongation of survival and is generally associated with the occurrence of toxicity effects. Antibody-based immunotherapy has been proposed for the treatment of PDAC, but its efficacy has so far proved limited. The proteoglycan glypican-1 (GPC1) may be a useful immunotherapeutic target because it is highly expressed on the surface of PDAC cells, whereas it is not expressed or is expressed at very low levels in benign neoplastic lesions, chronic pancreatitis, and normal adult tissues. Here, we developed and characterized a specific mouse IgM antibody (AT101) targeting GPC1. METHODS: We developed a mouse monoclonal antibody of the IgM class directed against an epitope of GPC1 in close proximity to the cell membrane. For this purpose, a 46 amino acid long peptide of the C-terminal region was used to immunize mice by an in-vivo electroporation protocol followed by serum titer and hybridoma formation. RESULTS: The ability of AT101 to bind the GPC1 protein was demonstrated by ELISA, and by flow cytometry and immunofluorescence analysis in the GPC1-expressing "PDAC-like" BXPC3 cell line. In-vivo experiments in the BXPC3 xenograft model showed that AT101 was able to bind GPC1 on the cell surface and accumulate in the BXPC3 tumor masses. Ex-vivo analyses of BXPC3 tumor masses showed that AT101 was able to recruit immunological effectors (complement system components, NK cells, macrophages) to the tumor site and damage PDAC tumor tissue. In-vivo treatment with AT101 reduced tumor growth and prolonged survival of mice with BXPC3 tumor (p < 0.0001). CONCLUSIONS: These results indicate that AT101, an IgM specific for an epitope of GPC1 close to PDAC cell surface, is a promising immunotherapeutic agent for GPC1-expressing PDAC, being able to selectively activate the complement system and recruit effector cells in the tumor microenvironment, thus allowing to reduce tumor mass growth and improve survival in treated mice.

Therapeutic drug monitoring and pharmacogenetics to tune imatinib exposure in gastrointestinal stromal tumor patients: hurdles and perspectives for clinical implementation.

Tweetable abstract Present evidence supports the use of intensified pharmacologic monitoring of #imatinib including #TherapeuticDrugMonitoring and #PGx to improve outcomes in patients with GI stromal tumor. Future studies need to address emerging questions to facilitate implementation in clinics.

Implementation of pre-emptive testing of a pharmacogenomic panel in clinical practice: Where do we stand?

Adverse drug reactions (ADRs) account for a large proportion of hospitalizations among adults and are more common in multimorbid patients, worsening clinical outcomes and burdening healthcare resources. Over the past decade, pharmacogenomics has been developed as a practical tool for optimizing treatment outcomes by mitigating the risk of ADRs. Some single-gene reactive tests are already used in clinical practice, including the DPYD test for fluoropyrimidines, which demonstrates how integrating pharmacogenomic data into routine care can improve patient safety in a cost-effective manner. The evolution from reactive single-gene testing to comprehensive pre-emptive genotyping panels holds great potential for refining drug prescribing practices. Several implementation projects have been conducted to test the feasibility of applying different genetic panels in clinical practice. Recently, the results of a large prospective randomized trial in Europe (the PREPARE study by Ubiquitous Pharmacogenomics consortium) have provided the first evidence that prospective application of a pre-emptive pharmacogenomic test panel in clinical practice, in seven European healthcare systems, is feasible and yielded a 30% reduction in the risk of developing clinically relevant toxicities. Nevertheless, some important questions remain unanswered and will hopefully be addressed by future dedicated studies. These issues include the cost-effectiveness of applying a pre-emptive genotyping panel, the role of multiple co-medications, the transferability of currently tested pharmacogenetic guidelines among patients of non-European origin and the impact of rare pharmacogenetic variants that are not detected by currently used genotyping approaches.

A comprehensive overview on antibody-drug conjugates: from the conceptualization to cancer therapy.

Antibody-Drug Conjugates (ADCs) represent an innovative class of potent anti-cancer compounds that are widely used in the treatment of hematologic malignancies and solid tumors. Unlike conventional chemotherapeutic drug-based therapies, that are mainly associated with modest specificity and therapeutic benefit, the three key components that form an ADC (a monoclonal antibody bound to a cytotoxic drug via a chemical linker moiety) achieve remarkable improvement in terms of targeted killing of cancer cells and, while sparing healthy tissues, a reduction in systemic side effects caused by off-tumor toxicity. Based on their beneficial mechanism of action, 15 ADCs have been approved to date by the market approval by the Food and Drug Administration (FDA), the European Medicines Agency (EMA) and/or other international governmental agencies for use in clinical oncology, and hundreds are undergoing evaluation in the preclinical and clinical phases. Here, our aim is to provide a comprehensive overview of the key features revolving around ADC therapeutic strategy including their structural and targeting properties, mechanism of action, the role of the tumor microenvironment and review the approved ADCs in clinical oncology, providing discussion regarding their toxicity profile, clinical manifestations and use in novel combination therapies. Finally, we briefly review ADCs in other pathological contexts and provide key information regarding ADC manufacturing and analytical characterization.

Surface antibody changes protein corona both in human and mouse serum but not final opsonization and elimination of targeted polymeric nanoparticles.

BACKGROUND: Nanoparticles represent one of the most important innovations in the medical field. Among nanocarriers, polymeric nanoparticles (PNPs) attracted much attention due to their biodegradability, biocompatibility, and capacity to increase efficacy and safety of encapsulated drugs. Another important improvement in the use of nanoparticles as delivery systems is the conjugation of a targeting agent that enables the nanoparticles to accumulate in a specific tissue. Despite these advantages, the clinical translation of therapeutic approaches based on nanoparticles is prevented by their interactions with blood proteins. In fact, the so-formed protein corona (PC) drastically alters the biological identity of the particles. Adsorbed activated proteins of the complement cascade play a pivotal role in the clearance of nanoparticles, making them more easily recognized by macrophages, leading to their rapid elimination from the bloodstream and limiting their efficacy. Since the mouse is the most used preclinical model for human disease, this work compared human and mouse PC formed on untargeted PNPs (uPNPs) and targeted PNPs (tPNPs), paying particular attention to complement activation. RESULTS: Mouse and human serum proteins adsorbed differently to PNPs. The differences in the binding of mouse complement proteins are minimal, whereas human complement components strongly distinguish the two particles. This is probably due to the human origin of the Fc portion of the antibody used as targeting agent on tPNPs. tPNPs and uPNPs mainly activate complement via the classical and alternative pathways, respectively, but this pattern did not affect their binding and internalization in macrophages and only a limited consumption of the activity of the human complement system was documented. CONCLUSIONS: The results clearly indicate the presence of complement proteins on PNPs surface but partially derived from an unspecific deposition rather than an effective complement activation. The presence of a targeting antibody favors the activation of the classical pathway, but its absence allows an increased activation of the alternative pathway. This results in similar opsonization of both PNPs and similar phagocytosis by macrophages, without an impairment of the activity of circulating complement system and, consequently, not enhancing the susceptibility to infection.

Cost-utility analysis and cross-country comparison of pharmacogenomics-guided treatment in colorectal cancer patients participating in the U-PGx PREPARE study.

OBJECTIVES: A cost-utility analysis was conducted to evaluate pharmacogenomic (PGx)-guided treatment compared to the standard-of-care intervention among patients diagnosed with colorectal cancer (CRC) in Italy. METHODS: Data derived from a prospective, open-label, block randomized clinical trial, as a part of the largest PGx study worldwide (355 patients in both arms) were used. Mortality was used as the primary health outcome to estimate life years (LYs) gained in treatment arms within a survival analysis context. PGx-guided treatment was based on established drug-gene interactions between capecitabine, 5-fluorouracil and irinotecan with DPYD and/or UGT1A1 genomic variants. Utility values for the calculation of Quality Adjusted Life Year (QALY) was based on Visual Analog Scale (VAS) score. Missing data were imputed via the Multiple Imputation method and linear interpolation, when possible, while censored cost data were corrected via the Replace-From-The-Right algorithm. The Incremental Cost-Effectiveness Ratio (ICER) was calculated for QALYs. Raw data were bootstrapped 5000 times in order to produce 95% Confidence Intervals based on non-parametric percentile method and to construct a cost-effectiveness acceptability curve. Cost differences for study groups were investigated via a generalized linear regression model analysis. Total therapy cost per patient reflected all resources expended in the management of any adverse events, including medications, diagnostics tests, devices, surgeries, the utilization of intensive care units, and wards. RESULTS: The total cost of the study arm was estimated at €380 (∼ US$416; 95%CI: 195-596) compared to €565 (∼ US$655; 95%CI: 340-724) of control arm while the mean survival in study arm was estimated at 1.58 (+0.25) LYs vs 1.50 (+0.26) (Log Rank test, X2 = 4.219, df=1, p-value=0.04). No statistically significant difference was found in QALYs. ICER was estimated at €13418 (∼ US$14695) per QALY, while the acceptability curve indicated that when the willingness-to-pay was under €5000 (∼ US$5476), the probability of PGx being cost-effective overcame 70%. The most frequent adverse drug event in both groups was neutropenia of severity grade 3 and 4, accounting for 82.6% of total events in the study arm and 65.0% in the control arm. Apart from study arm, smoking status, Body-Mass-Index and Cumulative Actionability were also significant predictors of total cost. Subgroup analysis conducted in actionable patients (7.9% of total patients) indicated that PGx-guided treatment was a dominant option over its comparator with a probability greater than 92%. In addition, a critical literature review was conducted, and these findings are in line with those reported in other European countries. CONCLUSION: PGx-guided treatment strategy may represent a cost-saving option compared to the existing conventional therapeutic approach for colorectal cancer patient management in the National Health Service of Italy.

Association of ADME gene polymorphisms on toxicity to CDK4/6 inhibitors in patients with HR+ HER2- metastatic breast cancer.

A wide interindividual variability in therapeutic response to cyclin-dependent kinases 4 and 6 inhibitors (CDKis) palbociclib, ribociclib and abemaciclib, among patients with HR+/HER2- metastatic breast cancer has been reported. This study explored the impact of genetic polymorphisms in ADME genes (responsible for drug absorption, distribution, metabolism, and elimination) on CDKis safety profiles in 230 patients. Selected endpoints include grade 3/4 neutropenia at day 14 of the first treatment cycle, early dose-limiting toxicities (DLTs), and dose reductions within the initial three cycles. Our analysis revealed associations between these endpoints and polymorphisms in CYP3A4, CYP3A5, ABCB1, and ABCG2 genes. Their impact on CDKis plasma concentrations (Ctrough) was also examined. Specifically, ABCB1 c.1236C>T and c.2677C>T polymorphisms correlated significantly with grade 3/4 neutropenia at day 14 (OR 3.94, 95% CI 1.32-11.75; p = 0.014 and OR 3.32, 95% CI 1.12-9.85; p = 0.030). Additionally, ABCB1 c.3435C>T was associated with an elevated risk of early DLTs and dose reductions (OR 3.28, 95% CI 1.22-8.84, p = 0.019; OR 2.60, 95% CI 1.20-5.60, p = 0.015). Carriers of the CYP3A4*22 allele also demonstrated in univariate a higher risk of early DLTs (OR 3.10, 95% CI 1.01-9.56, p = 0.049). Furthermore, individuals with the ABCB1 1236T-3435T-2677T(A) variant haplotype exhibited significant associations with grade 3/4 neutropenia at day 14 (OR 3.36, 95% CI 1.20-9.41; p = 0.021) and early DLTs in univariate (OR 3.08, 95% CI 1.19-7.95; p = 0.020). Homozygous carriers of the ABCB1 T-T-T(A) haplotype tended to have a higher mean ribociclib Ctrough (934.0 ng/mL vs. 752.0 ng/mL and 668.0 ng/mL). Regardless preliminary, these findings offer promising insights into the role of pharmacogenetic markers in CDKis safety profiles, potentially contributing to address the interindividual variability in CDKis responses.

Anti-HER2 Super Stealth Immunoliposomes for Targeted-Chemotherapy.

Liposomes play an important role in the field of drug delivery by virtue of their biocompatibility and versatility as carriers. Stealth liposomes, obtained by surface decoration with hydrophilic polyethylene glycol (PEG) molecules, represent an important turning point in liposome technology, leading to significant improvements in the pharmacokinetic profile compared to naked liposomes. Nevertheless, the generation of effective targeted liposomes-a central issue for cancer therapy-has faced several difficulties and clinical phase failures. Active targeting remains a challenge for liposomes. In this direction, a new Super Stealth Immunoliposomes (SSIL2) composed of a PEG-bi-phospholipids derivative is designed that stabilizes the polymer shielding over the liposomes. Furthermore, its counterpart, conjugated to the fragment antigen-binding of trastuzumab (Fab'TRZ -PEG-bi-phospholipids), is firmly anchored on the liposomes surface and correctly orients outward the targeting moiety. Throughout this study, the performances of SSIL2 are evaluated and compared to classic stealth liposomes and stealth immunoliposomes in vitro in a panel of cell lines and in vivo studies in zebrafish larvae and rodent models. Overall, SSIL2 shows superior in vitro and in vivo outcomes, both in terms of safety and anticancer efficacy, thus representing a step forward in targeted cancer therapy, and valuable for future development.

Machine learning to improve interpretability of clinical, radiological and panel-based genomic data of glioma grade 4 patients undergoing surgical resection.

BACKGROUND: Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. METHODS: By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. RESULTS: By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. CONCLUSIONS: The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling.

Clinical impact of body mass index on palbociclib treatment outcomes and effect on exposure.

The impact of body mass index (BMI) on treatment outcomes in patients with cancer is gaining increasing attention given the limited data available. The aim of this study was to investigate the contribution of BMI on the safety and efficacy profile of palbociclib in 134 patients with metastatic luminal-like breast cancer treated with palbociclib and endocrine therapy (ET). Normal-weight and underweight patients (BMI<25) were compared with overweight and obese (BMI≥25). Detailed clinical and demographic data were collected. Patients with a BMI<25 had a higher incidence of relevant-hematologic toxicities (p = 0.001), dose reduction events (p = 0.003), and tolerated lower dose intensities (p = 0.023) compared to patients with a BMI≥25. In addition, patients with a BMI<25 had significantly shorter progression-free survival (log-rank p = 0.0332). A significant difference was observed in the subgroup of patients for whom systemic palbociclib concentrations were available: patients with a BMI<25 had a 25% higher median minimum plasma concentrations (Cmin) compared to BMI≥25. This study provides compelling evidence for a clinically relevant contribution of BMI in discriminating a group of patients who experienced multiple toxicities that appeared to affect treatment adherence and lead to poorer survival. BMI could become a valuable tool for personalizing the starting dose of palbociclib to improve its safety and efficacy.

The use of therapeutic drug monitoring to highlight an over-looked drug-drug interaction leading to imatinib treatment failure.

BACKGROUND: Chronic oral anticancer therapies, are increasingly prescribed and present new challenges including the enhanced risk of overlooked drug-drug interactions (DDIs). Lengthy treatments and patients' management by different professionals can lead to serious prescribing errors that therapeutic drug monitoring (TDM) can help identifying thus allowing a more effective and safer treatment of patients with polypharmacy. OBJECTIVES: This report aims to exemplify how an intensified pharmacological approach could help in the clinical monitoring of patients on chronic treatments. METHODS: A patient with gastrointestinal stromal tumor was referred to our clinical pharmacology service due to tumor progression while on imatinib therapy. The investigation was based on TDM, pharmacogenetics, DDI evaluation and Circulating tumor DNA (ctDNA) analysis. The patient underwent repeated blood samplings to measure imatinib and norimatinib plasma concentrations through a validated LC-MS/MS method. Polymorphisms affecting genes involved in imatinib metabolism and transport were investigated using SNPline PCR Genotyping System. Drug-drug interactions were evaluated though Lexicomp. ctDNA analysis was performed on MiSeq platform. RESULTS: TDM analysis revealed that the patient was underexposed to imatinib (Cmin = 406 ng/mL; target Cmin = 1100 ng/mL). Subsequent DDI analysis highlighted a dangerous interaction with carbamazepine, via CYP3A4 and P-gp strong induction, omitted at the time of imatinib treatment start. No relevant pharmacogenetic variants were identified and appropriate compliance to treatment was ascertained. ctDNA monitoring was performed to assess potential tumor-related resistance to imatinib. Carbamazepine was cautiously switched to a non-interacting antiepileptic drug, restoting IMA plasma concentration (i.e. Cmin = 4298 ng/mL). The progression of the disease, which in turn led to the patient's death, was also witnessed by an increasing fraction of ctDNA in plasma. CONCLUSION: The active pharmacological monitoring allowed the identification of a dangerous previously over-looked DDI leading to IMA under-exposure. The switch to a different antiepileptic treatment, reversed the effect of DDI, restoring therapeutic IMA plasmatic concentrations.

Targeted chitosan nanobubbles as a strategy to down-regulate microRNA-17 into B-cell lymphoma models.

Introduction: MicroRNAs represent interesting targets for new therapies because their altered expression influences tumor development and progression. miR-17 is a prototype of onco-miRNA, known to be overexpressed in B-cell non-Hodgkin lymphoma (B-NHL) with peculiar clinic-biological features. AntagomiR molecules have been largely studied to repress the regulatory functions of up-regulated onco-miRNAs, but their clinical use is mainly limited by their rapid degradation, kidney elimination and poor cellular uptake when injected as naked oligonucleotides. Methods: To overcome these problems, we exploited CD20 targeted chitosan nanobubbles (NBs) for a preferential and safe delivery of antagomiR17 to B-NHL cells. Results: Positively charged 400 nm-sized nanobubbles (NBs) represent a stable and effective nanoplatform for antagomiR encapsulation and specific release into B-NHL cells. NBs rapidly accumulated in tumor microenvironment, but only those conjugated with a targeting system (antiCD20 antibodies) were internalized into B-NHL cells, releasing antagomiR17 in the cytoplasm, both in vitro and in vivo. The result is the down-regulation of miR-17 level and the reduction in tumor burden in a human-mouse B-NHL model, without any documented side effects. Discussion: Anti-CD20 targeted NBs investigated in this study showed physico-chemical and stability properties suitable for antagomiR17 delivery in vivo and represent a useful nanoplatform to address B-cell malignancies or other cancers through the modification of their surface with specific targeting antibodies.

LC-MS/MS Method for the Quantification of PARP Inhibitors Olaparib, Rucaparib and Niraparib in Human Plasma and Dried Blood Spot: Development, Validation and Clinical Validation for Therapeutic Drug Monitoring.

Poly (ADP-ribose) polymerase inhibitors (PARPis) are becoming increasingly meaningful in oncology, and their therapeutic drug monitoring (TDM) might be beneficial for patients. Several bioanalytical methods have been reported for PARPis quantification in human plasma, but advantages might be obtained using dried blood spot (DBS) as a sampling technique. Our aim was to develop and validate a liquid chromatography-tandem mass spectrometric (LC-MS/MS) method for olaparib, rucaparib, and niraparib quantification in both human plasma and DBS matrices. Additionally, we aimed to assess the correlation between the drug concentrations measured in these two matrices. DBS from patients was obtained using Hemaxis DB10 for volumetric sampling. Analytes were separated on a Cortecs-T3 column and detected with electrospray ionization (ESI)-MS in positive ionization mode. Validation was performed according to the latest regulatory guidelines, in the range (ng/mL) 140-7000 for olaparib, 100-5000 for rucaparib, and 60-3000 for niraparib, within the hematocrit (Hct) range 29-45%. The Passing-Bablok and Bland-Altman statistical analyses revealed a strong correlation between plasma and DBS for olaparib and niraparib. However, due to the limited amount of data, it was challenging to establish a robust regression analysis for rucaparib. To ensure a more reliable assessment, additional samples are required. The DBS-to-plasma ratio was used as a conversion factor (CF) without considering any patient-related hematological parameters. These results provide a solid basis for the feasibility of PARPis TDM using both plasma and DBS matrices.

Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab.

Pharmacogenomics studies how genes influence a person's response to treatment. When complex phenotypes are influenced by multiple genetic variations with little effect, a single piece of genetic information is often insufficient to explain this variability. The application of machine learning (ML) in pharmacogenomics holds great potential - namely, it can be used to unravel complicated genetic relationships that could explain response to therapy. In this study, ML techniques were used to investigate the relationship between genetic variations affecting more than 60 candidate genes and carboplatin-induced, taxane-induced, and bevacizumab-induced toxicities in 171 patients with ovarian cancer enrolled in the MITO-16A/MaNGO-OV2A trial. Single-nucleotide variation (SNV, formerly SNP) profiles were examined using ML to find and prioritize those associated with drug-induced toxicities, specifically hypertension, hematological toxicity, nonhematological toxicity, and proteinuria. The Boruta algorithm was used in cross-validation to determine the significance of SNVs in predicting toxicities. Important SNVs were then used to train eXtreme gradient boosting models. During cross-validation, the models achieved reliable performance with a Matthews correlation coefficient ranging from 0.375 to 0.410. A total of 43 SNVs critical for predicting toxicity were identified. For each toxicity, key SNVs were used to create a polygenic toxicity risk score that effectively divided individuals into high-risk and low-risk categories. In particular, compared with low-risk individuals, high-risk patients were 28-fold more likely to develop hypertension. The proposed method provided insightful data to improve precision medicine for patients with ovarian cancer, which may be useful for reducing toxicities and improving toxicity management.