Prediction of drug-induced kidney injury in drug discovery

Lisdexamfetamine dimesylate-exposition in male rats during the peripubertal period impairs inflammatory mechanisms, antioxidant activity, and apoptosis process in kidneys of male pubertal rats

Lisdexamfetamine dimesylate (LDX) is a prodrug of dextroamphetamine, which has been widely recommended for the treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). There are still no data in the literature relating the possible toxic effects of LDX in the kidney. Therefore, the present study aims to evaluate the effects of LDX exposure on morphological, oxidative stress, cell death and inflammation parameters in the kidneys of male pubertal Wistar rats, since the kidneys are organs related to the excretion of most drugs. For this, twenty male Wistar rats were distributed randomly into two experimental groups: LDX group-received 11,3 mg/kg/day of LDX; and Control group-received tap water. Animals were treated by gavage from postnatal day (PND) 25 to 65. At PND 66, plasma was collected to the biochemical dosage, and the kidneys were collected for determinations of the inflammatory profile, oxidative status, cell death, and for histochemical, and morphometric analyses. Our results show that there was an increase in the number of cells marked for cell death, and a reduction of proximal and distal convoluted tubules mean diameter in the group that received LDX. In addition, our results also showed an increase in MPO and NAG activity, indicating an inflammatory response. The oxidative status showed that the antioxidant system is working undisrupted and avoiding oxidative stress. Therefore, LDX-exposition in male rats during the peripubertal period causes renal changes in pubertal age involving inflammatory mechanisms, antioxidant activity and apoptosis process.

Growing attention on the toxicity of Chinese herbal medicine: a bibliometric analysis from 2013 to 2022

Introduction: Despite the clinical value of Chinese herbal medicine (CHM), restricted comprehension of its toxicity limits the secure and efficacious application. Previous studies primarily focused on exploring specific toxicities within CHM, without providing an overview of CHM's toxicity. The absence of a quantitative assessment of focal points renders the future research trajectory ambiguous. Therefore, this study aimed to reveal research trends and areas of concern for the past decade. Methods: A cross-sectional study was conducted on publications related to CHM and toxicity over the past decade from Web of Science Core Collection database. The characteristics of the publication included publication year, journal, institution, funding, keywords, and citation counts were recorded. Co-occurrence analysis and trend topic analysis based on bibliometric analysis were conducted on keywords and citations. Results: A total of 3,225 publications were analyzed. Number of annal publications increased over the years, with the highest number observed in 2022 (n = 475). The Journal of Ethnopharmacology published the most publications (n = 425). The most frequently used toxicity classifications in keywords were hepatotoxicity (n = 119) or drug-induced liver injury (n = 48), and nephrotoxicity (n = 40). Co-occurrence analysis revealed relatively loose connections between CHM and toxicity, and their derivatives. Keywords emerging from trend topic analysis for the past 3 years (2019-2022) included ferroptosis, NLRP3 inflammasome, machine learning, network pharmacology, traditional uses, and pharmacology. Conclusion: Concerns about the toxicity of CHM have increased in the past decade. However, there remains insufficient studies that directly explore the intersection of CHM and toxicity. Hepatotoxicity and nephrotoxicity, as the most concerned toxicity classifications associated with CHM, warrant more in-depth investigations. Apoptosis was the most concerned toxicological mechanism. As a recent increase in attention, exploring the mechanisms of ferroptosis in nephrotoxicity and NLRP3 inflammasome in hepatotoxicity could provide valuable insights. Machine learning and network pharmacology are potential methods for future studies.

Unveiling drug induced nephrotoxicity using novel biomarkers and cutting-edge preventive strategies

Drug-induced nephrotoxicity is still a significant obstacle in pharmacotherapy of various diseases and it accounts for around 25 % of serious side-effects reported after drug administration. Furthermore, some groups of drugs such as nonsteroidal anti-inflammatory drugs, antibiotics, antiviral drugs, antifungal drugs, immunosuppressants, and chemotherapeutic drugs have the "preference" for damaging the kidney and are often referred to as the kidney's "silent killer". Clinically, the onset of acute kidney injury associated with drug administration is registered in approximately 20 % of patients and many of them develop chronic kidney disease vulnerability. However, current knowledge about the mechanisms underlying this dangerous phenomenon is still insufficient with many unknowns. Hence, the valuable use of these drugs in clinical practice is significantly limited. The main aim of this study is to draw attention to commonly prescribed nephrotoxic drugs by clinicians or drugs bought over the counter. In addition, the complex relationship between immunological, vascular and inflammatory events that promote kidney damage is discussed. The practical use of this knowledge could be implemented in the engineering of novel biomarkers for early detection of drug-associated kidney damage such as Kidney Injury Molecule (KIM-1), lipocalin associated with neutrophil gelatinase (NGAL) and various microRNAs. In addition, the utilization of artificial intelligence (AI) for the development of computer algorithms that could detect kidney damage at an early stage should be further explored. Therefore, this comprehensive review provides a new outlook on drug nephrotoxicity that opens the door for further clinical research of novel potential drugs or natural products for the prevention of drug-induced nephrotoxicity and accessible education.

Utilization of 3D bioprinting technology in creating human tissue and organoid models for preclinical drug research - State-of-the-art

Rapid development of tissue engineering in recent years has increased the importance of three-dimensional (3D) bioprinting technology as novel strategy for fabrication functional 3D tissue and organoid models for pharmaceutical research. 3D bioprinting technology gives hope for eliminating many problems associated with traditional cell culture methods during drug screening. However, there is a still long way to wider clinical application of this technology due to the numerous difficulties associated with development of bioinks, advanced printers and in-depth understanding of human tissue architecture. In this review, the work associated with relatively well-known extrusion-based bioprinting (EBB), jetting-based bioprinting (JBB), and vat photopolymerization bioprinting (VPB) is presented and discussed with the latest advances and limitations in this field. Next we discuss state-of-the-art research of 3D bioprinted in vitro models including liver, kidney, lung, heart, intestines, eye, skin as well as neural and bone tissue that have potential applications in the development of new drugs.

The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.