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A growing number of population pharmacokinetic analyses of therapeutic monoclonal antibodies (mAbs) have been published in the scientific literature. The aims of this article are to summarize the findings from these studies and to relate the findings to the general pharmacokinetic and structural characteristics of therapeutic mAbs. A two-compartment model was used in the majority of the population analyses to describe the disposition of the mAb. Population estimates of the volumes of distribution in the central (V 1 ) and peripheral (V 2 ) compartments were typically small, with median (range) values of 3.1 (2.4–5.5) L and 2.8 (1.3–6.8) L, respectively. The estimated between-subject variability in the V 1 was usually moderate, with a median (range) coefficient of variation (CV) of 26% (12–84%). Between-subject variability in other distribution-related parameters such as the V 2 and intercompartmental clearance were often not estimated. Although the pharmacokinetic models used most frequently in the population analyses were models with linear clearance, other models with nonlinear, or parallel linear and nonlinear clearance pathways were also applied, as many therapeutic mAbs are eliminated via saturable target-mediated mechanisms. Population estimates of the maximum elimination rate (V max ) and the mAb concentration at which elimination was at half maximum for Michaelis-Menten-type elimination pathways varied considerably among the different therapeutic mAbs. However, estimates of the total clearance (CL) of mAbs with linear clearance characteristics and of the clearance of mAbs via the linear clearance pathway (CL L ) with parallel linear and nonlinear clearance were quite similar for the different mAbs and typically ranged from 0.2 to 0.5 L/day, which is relatively close to the estimated clearance of endogenous IgG of 0.21 L/day. The between-subject variability in the V max , CL and CL L was moderate to high, with estimated CVs ranging from 15% to 65%. Measures of body size were the covariates most commonly identified as influencing the pharmacokinetics of therapeutic mAbs. In summary, many features of the population pharmacokinetics of currently used therapeutic mAbs are similar, despite differences in their pharmacological targets and studied patient populations.

Pinocembrin is one of the most abundant flavonoids in propolis, and it may also be widely found in a variety of plants. In addition to natural extraction, pinocembrin can be obtained by biosynthesis. Biosynthesis efficiency can be improved by a metabolic engineering strategy and a two-phase pH fermentation strategy. Pinocembrin poses an interest for its remarkable pharmacological activities, such as neuroprotection, anti-oxidation, and anti-inflammation. Studies have shown that pinocembrin works excellently in treating ischemic stroke. Pinocembrin can reduce nerve damage in the ischemic area and reduce mitochondrial dysfunction and the degree of oxidative stress. Given its significant efficacy in cerebral ischemia, pinocembrin has been approved by China Food and Drug Administration (CFDA) as a new treatment drug for ischemic stroke and is currently in progress in phase II clinical trials. Research has shown that pinocembrin can be absorbed rapidly in the body and easily cross the blood–brain barrier. In addition, the absorption/elimination process of pinocembrin occurs rapidly and shows no serious accumulation in the body. Pinocembrin has also been found to play a role in Parkinson’s disease, Alzheimer’s disease, and specific solid tumors, but its mechanisms of action require in-depth studies. In this review, we summarized the latest 10 years of studies on the biosynthesis, pharmacological activities, and pharmacokinetics of pinocembrin, focusing on its effects on certain diseases, aiming to explore its targets, explaining possible mechanisms of action, and finding potential therapeutic applications.

The Bcl-2 protein has a vital function in controlling the programmed cell doom of mitochondria. If programmed cell death signals are obstructed, an imbalance between cell survival and death will occur, which is a significant reason for cancer. Therefore, the Bcl-2 protein was identified as a possible therapeutic target for carcinoma treatment. Herein, the Natural Products Atlas (NPAtlas) compounds were virtually screened, seeking potent inhibitors towards the Bcl-2 protein. The performance of AutoDock Vina software to predict the docking score and pose of the investigated compounds was first validated according to the available experimental data. Based on the validated AutoDock Vina parameters, the NPAtlas database was filtered against the Bcl-2 protein. The natural compounds with docking scores less than that of the venetoclax (calc. −10.6 kcal/mol) were submitted to MD simulations, followed by MM-GBSA binding energy calculations. According to MM-GBSA//200 ns MD simulations, saquayamycin F (NPA002200) demonstrated promising binding affinity with a ΔGbinding value of −53.9 kcal/mol towards the Bcl-2 protein when compared to venetoclax (ΔGbinding = −50.6 kcal/mol). The energetical and structural analyses showed a great constancy of the saquayamycin F inside the Bcl-2 protein active site. Moreover, the ADMET and drug-likeness features of the saquayamycin F were anticipated, indicating its good oral bioavailability. According to in silico computations, saquayamycin F is proposed to be used as a therapeutic agent against the wild-type Bcl-2 protein and warrants further experimental assays.

Antibiotics represent a pharmacotherapeutic group widely used in both human and veterinary medicine for which ecosurveillance has been continually recommended. It is urgent to rank the antibiotics and highlight those that may pose potential risk to the environment, a key step for the risk management. The absence of this type of contributions applied to the Portuguese reality supported the idea of compiling the data presented herein. With such purpose the most recent and representative data is used to draw a comparative contribution of each antimicrobial classes according to their intended use, i.e., in human versus veterinary medicine. The aim was to assess: (1) the amount and patterns of antimicrobials usage between human and animals; (2) the qualitative comparison between the antimicrobial classes used in each practice (human and veterinary) or specific use; (3) the potential to enter the environment, metabolism, mode of action and environmental occurrences. This manuscript will, thus, identify priorities for the environmental risk assessment, considering the ranking of the antimicrobials by their usage and potential environmental exposure. Ultimately, this study will serve as a basis for future monitoring programs, guiding the policy of regulatory agencies.

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The pharmacokinetics of vancomycin exhibit significant inter-individual variability, particularly among elderly patients. This study aims to develop a predictive model that integrates machine learning with population pharmacokinetics (popPK) to facilitate personalized medication management for this demographic. A retrospective analysis incorporating 33 features, including popPK parameters such as clearance and volume of distribution. A combination of multiple algorithms and Shapley Additive Explanations was utilized for feature selection to identify the most influential factors affecting drug concentrations. The performance of each algorithm with popPK parameters was superior to that without popPK parameters. Our final ensemble model, composed of support vector regression, light gradient boosting machine, and categorical boosting in a 6:3:1 ratio, included 16 optimized features. This model demonstrated superior predictive accuracy compared to models utilizing all features, with testing group metrics including an R 2 of 0.656, mean absolute error of 3.458, mean square error of 28.103, absolute accuracy within ± 5 mg/L of 81.82%, and relative accuracy within ± 30% of 76.62%. This study presents a rapid and cost-effective predictive model for estimating vancomycin plasma concentrations in elderly patients. The model offers a valuable tool for clinicians to accurately determine effective plasma concentration ranges and tailor individualized dosing regimens, thereby enhancing therapeutic outcomes and safety.

The systemic therapeutic utilisation of RNA interference (RNAi) is limited by the non-specific off-target effects, which can have severe adverse impacts in clinical applications. The accurate use of RNAi requires tumour-specific on-demand conditional activation to eliminate the off-target effects of RNAi, for which conventional RNAi systems cannot be used. Herein, a tumourous biomarker-activated RNAi platform is achieved through the careful design of RNAi prodrugs in extracellular vesicles (EVs) with cancer-specific recognition/activation features. These RNAi prodrugs are assembled by splitting and reconstituting the principal siRNAs into a hybridisation chain reaction (HCR) amplification machine. EVs facilitate the specific and efficient internalisation of RNAi prodrugs into target tumour cells, where endogenous microRNAs (miRNAs) promote immediate and autonomous HCR-amplified RNAi activation to simultaneously silence multiantenna hypoxia-related genes. With multiple guaranteed cancer recognition and synergistic therapy features, the miRNA-initiated HCR-promoted RNAi cascade holds great promise for personalised theranostics that enable reliable diagnosis and programmable on-demand therapy. The therapeutic application of small interfering RNA (siRNA) is challenging due to its non-specific targeting and delivery issues. Here, the authors report an endogenous micro-RNA guided and hybridisation chain reaction-promoted siRNA delivery system encapsulated in tumour-derived extracellular vesicles, with cancer-specific activation, and achieve silencing of hypoxia-related genes.

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0–24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.

Paraquat dichloride (methyl viologen; PQ) is an effective and widely used herbicide that has a proven safety record when appropriately applied to eliminate weeds. However, over the last decades, there have been numerous fatalities, mainly caused by accidental or voluntary ingestion. PQ poisoning is an extremely frustrating condition to manage clinically, due to the elevated morbidity and mortality observed so far and due to the lack of effective treatments to be used in humans. PQ mainly accumulates in the lung (pulmonary concentrations can be 6 to 10 times higher than those in the plasma), where it is retained even when blood levels start to decrease. The pulmonary effects can be explained by the participation of the polyamine transport system abundantly expressed in the membrane of alveolar cells type I, II, and Clara cells. Further downstream at the toxicodynamic level, the main molecular mechanism of PQ toxicity is based on redox cycling and intracellular oxidative stress generation. With this review we aimed to collect and describe the most pertinent and significant findings published in established scientific publications since the discovery of PQ, focusing on the most recent developments related to PQ lung toxicity and their relevance to the treatment of human poisonings. Considerable space is also dedicated to techniques for prognosis prediction, since these could allow development of rigorous clinical protocols that may produce comparable data for the evaluation of proposed therapies.