Comprehensive characterization and preclinical assessment of an imidazopyridine-based anticancer lead molecule.

Imidazopyridine scaffold holds significant pharmacological importance in the treatment of cancer. An in-house synthesized imidazopyridine-based molecule was found to have promising anticancer activity against breast cancer, lung cancer, and colon cancer. The molecule is an inhibitor of pyruvate kinase M2, the enzyme that elevates tumor growth, metastasis and chemoresistance by directly controlling tumor cell metabolism. Screening of the physicochemical properties of any lead molecules is essential to avoid failure in late-stage drug development. In this research, the physicochemical properties of the molecule including log P, log D, pKa, and plasma protein binding were assessed to check its drug-likeness. Plasma and metabolic stability of the molecule were also evaluated. Moreover, pharmacokinetic profiles of the lead molecule in Sprague-Dawley rats and in vitro metabolite identification studies were also performed. Finally, an in silico software, Pro-Tox-II, was used to predict toxicity of the molecule and its metabolites. Log P, Log D (pH 7.4), pKa, and plasma protein binding of the molecule were found to be 2.03%, 2.42%, 10.4%, and 98%, respectively. The molecule was stable in plasma and metabolic conditions. A total of nine new metabolites were identified and characterized. Cmax and t½ of this molecule were found to be 4016 ± 313.95 ng/mL and 9.57 ± 3.05 h, respectively. Based on the previously reported study and this finding, the molecule can be considered as a promising anticancer lead with potential drug-likeness properties. Further preclinical and clinical drug discovery studies may be initiated in continuation of this study in search of a potential anticancer lead.

Machine Learning models for detection and assessment of progression in Alzheimer's disease based on blood and cerebrospinal fluid biomarkers.

Machine-learning techniques were applied to human blood plasma and cerebrospinal fluid (CSF) biomarker data related to cognitive decline in Alzheimer's Disease (AD) patients available via Alzheimer Disease Neuroimaging Initiative (ADNI) study. We observed the accuracy of AD diagnosis is greatest when protein biomarkers from cerebrospinal fluid are combined with plasma proteins using Support Vector Machines (SVM); this is not improved by adding age and sex. The area under the receiver operator characteristic (ROC) curve for our model of AD diagnosis based on a full (unbiased) set of plasma proteins was 0.94 in cross-validation and 0.82 on an external validation (test) set. Taking plasma in combination with CSF, the model reaches 0.98 area under the ROC curve on the test set. Accuracy of prediction of risk of mild cognitive impairment progressing to AD is the same for blood plasma biomarkers as for CSF and is not improved by combining them or adding age and sex as covariates.Clinical relevance- The identification of accurate and cost-effective biomarkers to screen for risk of developing AD and monitoring its progression is crucial for improved understanding of its causes and stratification of patients for treatments under development. This paper demonstrates the feasibility of AD detection and prognosis based on blood plasma biomarkers.

Ovarian recurrence risk assessment using machine learning, clinical information, and serum protein levels to predict survival in high grade ovarian cancer.

In ovarian cancer, there is no current method to accurately predict recurrence after a complete response to chemotherapy. Here, we develop a machine learning risk score using serum proteomics for the prediction of early recurrence of ovarian cancer after initial treatment. The developed risk score was validated in an independent cohort with serum collected prospectively during the remission period. In the discovery cohort, patients scored as low-risk had a median time to recurrence (TTR) that was not reached at 10 years compared to 10.5 months (HR 4.66, p < 0.001) in high-risk patients. In the validation cohort, low-risk patients had a median TTR which was not reached compared to 4.7 months in high-risk patients (HR 4.67, p = 0.009). In advanced-stage patients with a CA125 < 10, low-risk patients had a median TTR of 68 months compared to 6 months in high-risk patients (HR 2.91, p = 0.02). The developed risk score was capable of distinguishing the duration of remission in ovarian cancer patients. This score may help guide maintenance therapy and develop innovative treatments in patients at risk at high-risk of recurrence.

Plasma Protein Binding Evaluations of Per- and Polyfluoroalkyl Substances for Category-Based Toxicokinetic Assessment.

New approach methodologies (NAMs) that make use of in vitro screening and in silico approaches to inform chemical evaluations rely on in vitro toxicokinetic (TK) data to translate in vitro bioactive concentrations to exposure metrics reflective of administered dose. With 1364 per- and polyfluoroalkyl substances (PFAS) identified as of interest under Section 8 of the U.S. Toxic Substances Control Act (TSCA) and concern over the lack of knowledge regarding environmental persistence, human health, and ecological effects, the utility of NAMs to understand potential toxicities and toxicokinetics across these data-poor compounds is being evaluated. To address the TK data deficiency, 71 PFAS selected to span a wide range of functional groups and physico-chemical properties were evaluated for in vitro human plasma protein binding (PPB) by ultracentrifugation with liquid chromatography-mass spectrometry analysis. For the 67 PFAS successfully evaluated by ultracentrifugation, fraction unbound in plasma (fup) ranged from less than 0.0001 (pentadecafluorooctanoyl chloride) to 0.7302 (tetrafluorosuccinic acid), with over half of the PFAS showing PPB exceeding 99.5% (fup < 0.005). Category-based evaluations revealed that perfluoroalkanoyl chlorides and perfluorinated carboxylates (PFCAs) with 6-10 carbons were the highest bound, with similar median values for alkyl, ether, and polyether PFCAs. Interestingly, binding was lower for the PFCAs with a carbon chain length of ≥11. Lower binding also was noted for fluorotelomer carboxylic acids when compared to their carbon-equivalent perfluoroalkyl acids. Comparisons of the fup value derived using two PPB methods, ultracentrifugation or rapid equilibrium dialysis (RED), revealed RED failure for a subset of PFAS of high mass and/or predicted octanol-water partition coefficients exceeding 4 due to failure to achieve equilibrium. Bayesian modeling was used to provide uncertainty bounds around fup point estimates for incorporation into TK modeling. This PFAS PPB evaluation and grouping exercise across 67 structures greatly expand our current knowledge and will aid in PFAS NAM development.

Preclinical safety assessment of photoluminescent metal quantum clusters stabilized with autologous serum proteins for host specific theranostics.

Host derived serum proteome stabilised red-emitting gold quantum clusters (or Au-QC-NanoSera or QCNS) of size range ~2 nm have been synthesised in a first reported study. The host serum was taken from bovine, murine and human origins to establish the proof of concept. In-vitro biocompatibility with normal murine L929 fibroblast cells and radiosensitisation ability against PLC/PRF/5 hepatoma cells was established. A concentration dependant radiosensitisation effect of QCNS at differential γ-radiation doses was observed with almost 90% killing of cancer cells at a radiation dose of 5Gy. Acute and subacute safety, and non-immunogenicity of autologously derived QCNS was established in in-bred C57BL/6 mice. The biodistribution analysis revealed that the QCNS were effectively cleared from the body over a course of 28 days and were found to pose no major threat to the proper functioning and morphology of the mice.

Integrative assessment of immunity, health-state, growth and survival of Magellanic penguin chicks in a colony exposed to ecotourism.

Accumulating reports of negative impacts of tourist activities on wildlife emphasize the importance of closely monitoring focal populations. Although some effects are readily noticed, more subtle ones such as changes in physiological functions of individuals might go overlooked. Based on evidence of altered physiology associated with ecotourism on Magellanic penguins Spheniscus magellanicus, here we performed an integrated assessment using a diverse physiological toolkit together with more traditional fitness-related measures to better understand mechanisms and potential consequences. Chicks exposed to tourism showed altered immune parameters and elevated flea prevalence, reinforcing previous findings. Tourism-exposed female, but not male, chicks also showed relatively lower hematocrit and plasma protein levels, providing evidence consistent with a sex-specific response to tourist visitation. Physiological alterations detected in tourism-exposed young chicks (week 1-2) were maintained and the effect on flea infestation increased during the study period (week 4-5 of post-hatch). Despite the effects on physiology, these did not seem to translate into immediate fitness costs. No detectable tourism effects were found on brood sex ratios, chick growth and body condition, and survival until week 5-6 post-hatch. We detected no effects on reproductive output and only a marginal effect on nest survival during incubation despite previous reports of tourism-associated alterations in stress indices of adults. This disconnection could result if the physiological changes are not strong enough to impact fitness, if effects balance each other out, or if changes are part of a copying strategy. Alternatively, the physiological alterations might only show impacts later in the brooding cycle or even after chick emancipation from their parents. Our results suggest that integrative monitoring of potential anthropogenic impacts on wildlife should include evaluation of physiological mechanisms and individual-level responses in populations exposed to human activities.

Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors.

BACKGROUND: Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis. RESEARCH QUESTION: What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model? STUDY DESIGN AND METHODS: Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model. RESULTS: In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold. INTERPRETATION: The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.

Addressing the Accuracy of Plasma Protein Binding Measurement for Highly Bound Compounds Using the Dilution Method.

Currently, regulatory guidelines recommend using 0.01 as the lower limit of plasma fraction unbound (fu) for prediction of drug-drug interactions (DDI) to err on the conservative side. One way to increase experimental fu of highly bound compounds is to dilute the plasma. With the dilution method, a diluted fu, or fu,d, of ≥ 0.01 can be achieved by adjusting the dilution factor. The undiluted fu can be calculated from fu,d and be used for DDI prediction. In this study, the dilution method was evaluated, and the results showed that it gave similar fu values as those determined using the pre-saturation method without plasma dilution. The dilution method enables generation of accurate fu values and alignment with the regulatory recommendation of reportable fu values of ≥ 0.01 for DDI prediction. We recommend using the dilution method to bridge the regulatory recommended fu limit of 0.01 for DDI prediction and the pre-saturation or equivalent methods for definitive plasma protein binding studies. As the pharmaceutical industry continues to generate high quality PPB data, regulatory agencies will gain confidence in the accuracy of fu measurements for highly bound compounds, and the fu lower limit may no longer be needed in the future.

A STUDY OF THE ASSESSMENT OF SERUM ADROPIN LEVEL AS A RISK FACTOR OF ISCHAEMIC HEART DISEASE IN TYPE 2 DIABETES MELLITUS CASES.

Adropin is a peptide hormone that was first identified in 2008 and was first thought to have a significant role in the balance of fatty acids and glucose in peripheral tissues. We look at the relationship between adropin and diabetes individuals' ischemic heart disease. The objective of the study is to evaluate the serum adropin level as a potential indicator of ischemic heart disease in people with type 2 diabetes mellitus. The 90 participants in this case-control study were split into three groups: Group (I) consisted of 30 T2DM patients with ischemic heart disease Group (II) consisted of 30 T2DM patients without ischaemic heart disease Group (III) consisted of 30 healthy persons as the control group. HbA1c, lipid profile (cholesterol, triglycerides, LDL-C, HDL), HOMA IR serum creatinine, AST, ALT, and serum adropin were also evaluated. Fasting plasma glucose, 2h postprandial plasma glucose, Carotid artery intimal thickness using ultrasound, and Carotid artery intimal thickness were also measured. Patients with diabetes who did not have ischemic heart disease had a statistically significant rise in serum Adropin hormone (p value 0.001), with values of (26.867 10.037) ng/L and (87.500 40.509) ng/L, respectively. Additionally, there was a bad correlation between serum adropin and CIMT and fasting insulin. Assessment of serum adropin levels may serve as a risk indicator for the emergence of ischemic heart disease in people with type 2 diabetes mellitus.

ANALYSIS OF SERUM PROTEINS IN HEALTHY GIANT PANDAS (AILUROPODA MELANOLEUCA) UNDER MANAGED CARE.

Electrophoresis is a practical diagnostic tool for the identification of changes in serum protein fractions, which can be associated with a variety of diseases. Protein electrophoresis studies in Ursidae are limited, and currently no published fraction values are available for the giant panda (Ailuropoda melanoleuca). The aim of this study was to describe the serum protein fractions in the giant panda using both capillary zone electrophoresis (CZE) and standard agarose gel electrophoresis (AGE) techniques. Serum samples from nine healthy giant pandas (n = 19) were used for this study. Samples were evaluated using CZE and standard AGE. The CZE procedure successfully resolved serum proteins into seven fractions: prealbumin; albumin; and α1-, α2-, ß1-, ß2-, and γ-globulin; while AGE separated serum into only six protein fractions: prealbumin; albumin; α1-, α2-, and ß-globulins; and γ-globulin. These data will serve as a preliminary baseline for further studies and provide insight for the medical management of giant pandas.

Considerations and recommendations for assessment of plasma protein binding and drug-drug interactions for siRNA therapeutics.

At the time of writing, although siRNA therapeutics are approved for human use, no official regulatory guidance specific to this modality is available. In the absence of guidance, preclinical development for siRNA followed a hybrid of the small molecule and biologics guidance documents. However, siRNA differs significantly from small molecules and protein-based biologics in its physicochemical, absorption, distribution, metabolism and excretion properties, and its mechanism of action. Consequently, certain reports typically included in filing packages for small molecule or biologics may benefit from adaption, or even omission, from an siRNA filing. In this white paper, members of the 'siRNA working group' in the IQ Consortium compile a list of reports included in approved siRNA filing packages and discuss the relevance of two in vitro reports-the plasma protein binding evaluation and the drug-drug interaction risk assessment-to support siRNA regulatory filings. Publicly available siRNA approval packages and the literature were systematically reviewed to examine the role of siRNA plasma protein binding and drug-drug interactions in understanding pharmacokinetic/pharmacodynamic relationships, safety and translation. The findings are summarized into two decision trees to help guide industry decide when in vitro siRNA plasma protein binding and drug-drug interaction studies are warranted.

Assessment of alteration in antiviral plasma concentration across dialysis days: computational and analytical study.

Aim: Protein-bound uremic toxins (PBUTs) may displace drugs from the plasma proteins and render them more liable to clearance. This study aims to investigate the possible interplay between PBUTs and directly acting antivirals (DAAs). Methods: PBUT plasma protein binding was compared to those of paritaprevir (PRT), ombitasivir (OMB) and ritonavir (RTV) in silico to assess the possible competitive displacement. The three drugs were LC-MS/MS determined in seven patients across dialysis and non-dialysis days and results were compared. Results & conclusion: Results showed that the PBUT exhibited a lower binding than DAA reducing the liability of their competitive displacement. This was echoed by an unaltered plasma concentration across dialysis days. Results may indicate that PBUT accumulation may have limited effect on disposition of DAA.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Solid-phase microextraction for assessment of plasma protein binding, a complement to rapid equilibrium dialysis.

Aim: Determination of plasma protein binding (PPB) is considered vital for better understanding of pharmacokinetic and pharmacodynamic activities of drugs due to the role of free concentration in pharmacological response. Methodology & results: Solid-phase microextraction (SPME) was investigated for measurement of PPB from biological matrices and compared with a gold standard approach (rapid equilibrium dialysis [RED]). Discussion & conclusion: SPME-derived values of PPB correlated well with literature values, and those determined by RED. Respectively, average protein binding across three concentrations by RED and SPME was 33.1 and 31.7% for metoprolol, 89.0 and 86.6% for propranolol and 99.2 and 99.0% for diclofenac. This study generates some evidence for SPME as an alternative platform for the determination of PPB.

Value of galectin-3 in the diagnosis of acute coronary syndrome and the assessment of coronary artery lesions.

Aim: To investigate the value of galectin-3 in the diagnosis of acute coronary syndrome (ACS) and the assessment of coronary artery lesions. Methodology: This study recruited 157 patients with coronary artery disease where 102 and 55 of them were subsequently grouped as ACS and non-ACS, respectively. The severity of coronary artery lesions was evaluated by Gensini score and the number of vessels involved. Results: Receiver operator characteristics analyses of galectin-3 yielded an area under the curve of 0.679 in diagnosing ACS. The galectin-3 levels were correlated with Gensini score and the number of vessels involved. Conclusion: Our study demonstrated that galectin-3 is an effective auxiliary biomarker for the diagnosis of ACS and assessment of coronary artery lesions.

Polyclonal hypergammaglobulinaemia: assessment, clinical interpretation, and management.

This Review outlines a practical approach to assessing and managing polyclonal hypergammaglobulinaemia in adults. Polyclonal hypergammaglobulinaemia is most commonly caused by liver disease, immune dysregulation, or inflammation, but can also provide an important diagnostic clue of rare diseases such as histiocyte disorders, autoimmune lymphoproliferative syndrome, Castleman disease, and IgG4-related disease. Causes of polyclonal hypergammaglobulinaemia can be divided into eight categories: liver disease, autoimmune disease and vasculitis, infection and inflammation, non-haematological malignancy, haematological disorders, IgG4-related disease, immunodeficiency syndromes, and iatrogenic (from immunoglobulin therapy). Measuring serum concentrations of C-reactive protein and IgG subclasses are helpful in diagnosis. IL-6-mediated inflammation, associated with persistently elevated C-reactive protein concentrations (≥30 mg/L), is an important driver of polyclonal hypergammaglobulinaemia in some cases. Although the presence of markedly elevated serum IgG4 concentrations (>5 g/L) is around 90% specific for diagnosing IgG4-related disease, mildly elevated serum IgG4 concentrations are seen in many conditions. In most cases, managing polyclonal hypergammaglobulinaemia simply involves treating the underlying condition. Rarely, however, polyclonal hypergammaglobulinaemia can lead to hyperviscosity, requiring plasmapheresis.

Interaction of pharmaceutical metabolites with blood proteins and membrane lipids in the view of bioconcentration: A preliminary study based on in vitro assessment.

Pharmaceuticals pose a real threat to the environment, which has been proven in many studies to date. However, still little is known about the transformation products (TPs) of these compounds, which can also interact with organisms, causing adverse effects like noticeable toxicity or bioconcentration. Many recent works confirm that metabolites of pharmaceuticals are present in the environment, and preliminary studies suggest that they may be equally dangerous to or even more so than their parent compounds. Additionally, it has been proven that some of them have high hydrolytic stability, thus they may be persistent in the environment. This property also increases the likelihood that these compounds will be uptaken and accumulated in the tissues of organisms. Therefore, the aim of the present study was to preliminarily estimate the affinity of the transformation products of selected drugs for blood proteins and cell membrane-forming lipids, considered as important sorption phases during distribution in a living organism. In this study, it was shown that although the examined metabolites do not have a strong affinity for membrane lipids, they exhibit relatively strong binding to proteins, which may considerably affect the distribution of TPs in an organism and may indicate a non-classical process of bioconcentration. The results obtained confirm that the TPs of pharmaceuticals should be given much more attention and their potential for bioconcentration should be further determined.

Computational Assessment of Chito-Oligosaccharides Interactions with Plasma Proteins.

It is widely recognized that chitin and chitosan are potential sources of bioactive materials and that their oligosaccharides reveal various biological activities (including antimicrobial) that are correlated with their structures and physicochemical properties. This study uses the molecular docking approach to assess the interactions of small chito-oligosaccharides (MW< 1500 Da) with plasma proteins in order to obtain information regarding their fate of distribution in the human organism. There are favorable interactions of small chito-oligomers with plasma proteins, the interactions with human serum albumin being stronger than those with α-1-acid glycoprotein. The interaction energies increase with increasing the molecular weight, decrease with increasing deacetylation degrees and are reliant on the deacetylation pattern. This study could inform the application of chito-oligosaccharides with varying molecular weights, degrees, and patterns of deacetylation in human health.

Evaluation of models for predicting pediatric fraction unbound in plasma for human health risk assessment.

Pediatric physiologically based pharmacokinetic (PBPK) models facilitate the prediction of PK parameters in children under specific exposure conditions. Pharmacokinetic outcomes are highly sensitive to fraction unbound in plasma (fup) as incorporated into PBPK models. Rarely is fup in children (fupchild) experimentally derived and prediction is based upon fup in adults (fupadult) as well as a ratio of plasma protein concentrations between children and adults. The objectives were to (i) evaluate protein concentration vs. age profile derived from ontogeny models, (ii) assess predictive performances of fup ontogeny models, and (iii) determine overall uncertainty in fupchild prediction resulting from a combination of quantitative structure-property relationship (QSPR) model and ontogeny models. The plasma albumin and alpha-acid glycoprotein (AAG) concentration data for pediatrics and fupchild and fupadult data were obtained from literature. The protein concentration vs. age profile derived from ontogeny models were compared to observed levels. Fupchild values were calculated according to ontogeny models using both observed and QSPR-predicted fupadult as inputs and predictive performances of ontogeny models assessed by comparing predicted fupchild to observed values. Protein concentrations vs. age profiles derived from non-linear equations were more congruent with observed albumin levels than linear or step-wise models. When observed fupadult values were used as input, the fupchild data were under-predicted with average fold error (AFE) amounts ranging 0.79-0.81 and 0.77-0.97 for albumin and AAG ontogeny models, respectively. When QSPR-predicted fupadult values were used as input, AFE of fupchild ranged 1.2-1.35 and 0.98-1.2 for albumin and AAG models, respectively. The choice of ontogeny model with respect to prediction accuracy is more important for AAG, highly bound compounds and infants. For these compounds and scenarios, experimental determination of fupchild for inclusion into a pediatric PBPK model is necessary to have confidence in PBPK model outputs.