In Silico Toxicology (Switzerland)

Abstract This work takes place at two interacting levels. One is a reflection on how to define therapeutic efficacy. The other one is a series of comparative simulation experiments confronting two definitions of therapeutic efficacy. In real-world settings, treatment efficacy is typically estimated through randomized controlled trials (RCTs), yielding what we define as gross efficacy (GE). However, biases introduced by the enrollment process and imperfect results of randomization can affect GE estimates. In contrast, in a model informed drug development (MIDD) perspective, an in silico clinical trial (ISCT) is unbiased. It is conducted via computational simulations using a quantitative systems pharmacology (QSP) model of disease and treatments and a virtual population. The two (or more) compared treatments are given in turn to the same virtual patient, with the same environment, at the same time, in an as close as possible representation of the whole population of interest, resulting in the prediction of the net efficacy (NE). Because inter-individual and inter-occasion differences are removed, the NE prediction contains no external bias. In order to explore the two paradigms, GE and NE, clinical trials were simulated using a disease model of advanced EGFR-mutated lung adenocarcinoma (aLUAD), a subtype of Non Small Cell Lung Cancer (NSCLC) and two treatment models, one for the investigational treatment, osimertinib in monotherapy, (A), and one for the control treatment, chemotherapy agents cisplatin and pemetrexed (B). The model used in the current simulations was a simplified model of a model that has proven its credibility in prospectively and blindly predicting accurately the results of real world phase 3 clinical trials, after a thorough validation procedure. The simplified model was applied in two different settings to a large virtual population. The first setting was designed to compute the net efficacy of treatment A versus B. The second setting was set-up to mimic 1,000 real-life, parallel group design RCTs, based on standard sampling theory to compare A and B. The number of patients per arm was selected to be equal to or greater than the currently published trials. As opposed to the first approach, the approach mimicking the RCT provides measures of the gross efficacy. A single RCT was randomly drawn to simulate a phase 3 trial which concludes a clinical development program. While the mean hazard ratios of clinical outcomes calculated by the GE approach and the hazard ratio of clinical outcome given by the NE approach are not very different, they could nevertheless lead to quite different assessments of the population benefit of the new treatment compared to a comparator and to different regulatory decisions. Further, some values of GE from the distribution of “observed” GE were rather far from the mean GE and the NE. The convergence of the GE estimate takes more than a couple of RCTs, showing that a single phase 3 trial is not enough. However, the number of trials sufficient for convergence is impracticable. This work also shows that a randomized phase 3 trial of a genuinely effective therapeutic can easily fail to demonstrate a difference between the two groups of treatment due solely to sampling fluctuations, incorrectly leading to the discontinuation of the investigational drug because of lack of chance. These findings suggest that while ISCTs cannot replace real-world RCTs, they provide valuable insights for establishing clinical development strategy, trial design, trial monitoring, and trial results interpretation.

Seborrheic dermatitis is a dermatological syndrome which was first described in 1887 at about the time that tobacco smoking was becoming widespread in public places. Since 1978 the study of a familial seborrheic dermatitis revealed an immune reaction to the hapten nicotine of tobacco smoke. At that time, the efficacy of a sodium cromoglycate ointment showed a potential involvement of tissue mast cells. Later the use of a soap and ointment based on Dead Sea salts proved to be effective. At the same time, several laboratories detected histamine locally in patients afflicted with seborrheic dermatitis. Dead Sea and Zechstein salts contain about 30% of magnesium chloride. Recently, the use of a magnesium chloride ointment showed a significant efficacy. Future research should investigate the exact role of magnesium chloride on tissue mast cells and especially their ion channels. The stabilization of tissue mast cells appears as a new and promising research to help patients with seborrheic dermatitis and probably other dermatological syndromes as atopic dermatitis.

Background: C-reactive protein (CRP) is linked to inflammation and elevated cardiovascular risk in metabolic disorders. Metformin has been shown to lower CRP concentrations. However, it is still unclear whether elevated CRP levels could be modulated by metformin. Objective: This study aims to evaluate the effects of metformin and its combinations on CRP levels in T2DM patients. Patients and Methods: a prospective comparative study was carried out at Tishreen University Hospital and some private diabetic clinics in Lattakia, Syria during the period between April 2020 and March 2022.The study included three groups of patients: group I (31 patients, 32.6%) received metformin at a dose of 1,000mg/d, group II (30 patients, 31.6%) received modified release gliclazide (60mg/d) and group III (34 patients, 35.8%) received metformin with sitagliptin (50/1000mg/d). CRP levels were measured at baseline and after 3 months of treatment. Results: a total of 95 patients, 55males (57.90%) and 40 females (42.1%) with a mean age of 50.72±6.6 years were included in the study. There were no significant differences between the three groups regarding age, sex, and BMI (p > 0.05). There was a significant decrease in FPG after treatment with metformin, gliclazide, and metformin with sitagliptin; 21.92%, 18.75%, and 24.39% respectively, p<0.0001. Metformin, gliclazide, and metformin with sitagliptin significantly reduced HbA1c by 18.7%, 17.36%, and 21.92% respectively. There was a significant change in CRP levels from baseline after receiving metformin (2.06±0.8 vs 3.46±0.9, p<0.001), and metformin with sitagliptin (1.84±0.6 vs 3.30±0.7, p:0<0001). However, the reduction in gliclazide group wasn’t significant (2.97±0.6 vs 3.14±1.1, p=0.09). Reduction in CRP levels wasn't influenced by age, sex or BMI, and was independent of glycemic control (p > 0.05). Conclusion: We demonstrated that metformin and its combination with sitagliptin have a favorable beneficial effect on inflammation marker CRP in patients with T2DM.

Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new Salmonella mutagenicity dataset with 8,290 unique chemical structures utilizing MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the Salmonella mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest.

The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis. A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules. The area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984. This performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.

Abstract Honglong Zhao is a postdoctoral researcher at the Chinese Academy of Sciences Center for Excellence in Molecular Plant Sciences. There, he studies plant systems biology and works on the mathematical model construction of primary metabolism in C3 photosynthetic leaves. He received a PhD in Botany in 2020 working with Prof. Xinguang Zhu.

In vitro digestions are essential for determining the bioavailability of compounds, such as nutrients. We have developed a cell-free, miniaturized enzymatic digestive system, employing three micromixers connected in series to mimic the digestive functions of the mouth, stomach and small intestine. This system continuously processes samples, e.g. containing nutrients, to provide a constant flow of digested materials which may be presented to a subsequent gut-on-a-chip absorption module, containing living human intestinal cells. Our system incorporates three-compartment enzymatic digestion, one of the key functions of the gastrointestinal tract. In each of these compartments, we modify the chemical environment, including pH, buffer, and mineral composition, to closely mimic the local physiological environment and create optimal conditions for digestive processes to take place. It will therefore provide an excellent addition to existing gut-on-a-chip systems, providing the next step in determining the bio-availability of orally administered compounds in a fast and continuous-flow ex vivo system. In this paper, we demonstrate enzymatic digestion in each separate compartment using compounds, starch and casein, as model nutrients. The use of transparent, microfluidic micromixers based on chaotic advection, which can be probed directly with a microscope, enabled enzyme kinetics to be monitored from the very start of a reaction. Furthermore, we have digested lactoferrin in our system, demonstrating complete digestion of this milk protein in much shorter times than achievable with standard in vitro digestions using batch reactors.