Meiotic recombination mirrors patterns of germline replication in mice and humans.

Genetic recombination generates novel trait combinations, and understanding how recombination is distributed across the genome is key to modern genetics. The PRDM9 protein defines recombination hotspots; however, megabase-scale recombination patterning is independent of PRDM9. The single round of DNA replication, which precedes recombination in meiosis, may establish these patterns; therefore, we devised an approach to study meiotic replication that includes robust and sensitive mapping of replication origins. We find that meiotic DNA replication is distinct; reduced origin firing slows replication in meiosis, and a distinctive replication pattern in human males underlies the subtelomeric increase in recombination. We detected a robust correlation between replication and both contemporary and historical recombination and found that replication origin density coupled with chromosome size determines the recombination potential of individual chromosomes. Our findings and methods have implications for understanding the mechanisms underlying DNA replication, genetic recombination, and the landscape of mammalian germline variation.

The emerging challenge of Enterococcus faecalis endocarditis after transcatheter aortic valve implantation: time for innovative treatment approaches.

SUMMARYInfective endocarditis (IE) is a life-threatening infection that has nearly doubled in prevalence over the last two decades due to the increase in implantable cardiac devices. Transcatheter aortic valve implantation (TAVI) is currently one of the most common cardiac procedures. TAVI usage continues to exponentially rise, inevitability increasing TAVI-IE. Patients with TAVI are frequently nonsurgical candidates, and TAVI-IE 1-year mortality rates can be as high as 74% without valve or bacterial biofilm removal. Enterococcus faecalis, a historically less common IE pathogen, is the primary cause of TAVI-IE. Treatment options are limited due to enterococcal intrinsic resistance and biofilm formation. Novel approaches are warranted to tackle current therapeutic gaps. We describe the existing challenges in treating TAVI-IE and how available treatment discovery approaches can be combined with an in silico "Living Heart" model to create solutions for the future.

Inference of alveolar capillary network connectivity from blood flow dynamics.

The intricate lung structure is crucial for gas exchange within the alveolar region. Despite extensive research, questions remain about the connection between capillaries and the vascular tree. We propose a computational approach combining three-dimensional morphological modeling with computational fluid dynamics simulations to explore alveolar capillary network connectivity based on blood flow dynamics.We developed three-dimensional sheet-flow models to accurately represent alveolar capillary morphology and conducted simulations to predict flow velocities and pressure distributions. Our approach leverages functional features to identify plausible system architectures. Given capillary flow velocities and arteriole-to-venule pressure drops, we deduced arteriole connectivity details. Preliminary analyses for non-human species indicate a single alveolus connects to at least two 20 µm arterioles or one 30 µm arteriole. Hence, our approach narrows down potential connectivity scenarios, but a unique solution may not always be expected.Integrating our blood flow model results into our previously published gas exchange application, Alvin, we linked these scenarios to gas exchange efficiency. We found that increased blood flow velocity correlates with higher gas exchange efficiency.Our study provides insights into pulmonary microvasculature structure by evaluating blood flow dynamics, offering a new strategy to explore the morphology-physiology relationship that is applicable to other tissues and organs. Future availability of experimental data will be crucial in validating and refining our computational models and hypotheses.

Modeling the Drug delivery Lifecycle of PLG Nanoparticles Using Intravital Microscopy.

Polylactide-co-glycolide (PLG) nanoparticles hold immense promise for cancer therapy due to their enhanced efficacy and biodegradable matrix structure. Understanding their interactions with blood cells and subsequent biodistribution kinetics is crucial for optimizing their therapeutic potential. In this study, three doxorubicin-loaded PLG nanoparticle systems are synthesized and characterized, analyzing their size, zeta potential, morphology, and in vitro release behavior. Employing intravital microscopy in 4T1-tumor-bearing mice, real-time blood and tumor distribution kinetics are investigated. A mechanistic pharmacokinetic model is used to analyze biodistribution kinetics. Additionally, flow cytometry is utilized to identify cells involved in nanoparticle hitchhiking. Following intravenous injection, PLG nanoparticles exhibit an initial burst release (<1 min) and rapidly adsorb to blood cells (<5 min), hindering extravasation. Agglomeration leads to the clearance of one carrier species within 3 min. In stable dispersions, drug release rather than extravasation remains the dominant pathway for drug elimination from circulation. This comprehensive investigation provides valuable insights into the interplay between competing kinetics that influence the lifecycle of PLG nanoparticles post-injection. The findings advance the understanding of nanoparticle behavior and lay the foundation for improved cancer therapy strategies using nanoparticle-based drug delivery systems.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

Importance of Considering Fed-State Gastrointestinal Physiology in Predicting the Reabsorption of Enterohepatic Circulation of Drugs.

PURPOSE: The purpose of this study was to develop a simulation model for the pharmacokinetics (PK) of drugs undergoing enterohepatic circulation (EHC) with consideration to the environment in the gastrointestinal tract in the fed state in humans. The investigation particularly focused on the necessity of compensating for the permeability rate constant in the reabsorption process in consideration of drug entrapment in bile micelles. METHODS: Meloxicam and ezetimibe were used as model drugs. The extent of the entrapment of drugs inside bile micelles was evaluated using the solubility ratio of Fed State Simulated Intestinal Fluid version 2 (FeSSIF-V2) to Fasted State Simulated Intestinal Fluid version 2 (FaSSIF-V2). Prediction accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) value, calculated from the observed and predicted oral PK profiles. RESULTS: The solubilization of ezetimibe by bile micelles was clearly observed while that of meloxicam was not. Assuming that only drugs in the free fraction of micelles permeate through the intestinal membrane, PK simulation for ezetimibe was performed in both scenarios with and without compensation by the permeation rate constant. The MAPE value of Zetia® tablet, containing ezetimibe, was lower with compensation than without compensation. By contrast, Mobic® tablet, containing meloxicam, showed a relatively low MAPE value even without compensation. CONCLUSION: For drugs which undergo EHC and can be solubilized by bile micelles, compensating for the permeation rate constant in the reabsorption process based on the free fraction ratio appears an important factor in increasing the accuracy of PK profile prediction.

An improved workflow for the development of MS-compatible liquid chromatography assay purity and purification methods by using automated LC Screening instrumentation and in silico modeling.

The development of liquid chromatography UV and mass spectrometry (LC-UV-MS) assays in pharmaceutical analysis is pivotal to improve quality control by providing critical information about drug purity, stability, and presence and identity of byproducts and impurities. Analytical method development of these assays is time-consuming, which often causes it to become a bottle neck in drug development and poses a challenge for process chemists to quickly improve the chemistry. In this study, a systematic and efficient workflow was designed to develop purity assay and purification methods for a wide range of compounds including peptides, proteins, and small molecules with MS-compatible mobile phases (MP) by using automated LC screening instrumentation and in silico modeling tools. Initial LC MPs and chromatography column screening experiments enabled quick identification of conditions which provided the best resolution in the vicinity of the target compounds, which is further optimized using computer-assisted modeling (LC Simulator from ACD/Labs). The experimental retention times were in good agreement with the predicted retention times from LC Simulator (ΔtR < 7%). This workflow presents a practical workflow to significantly expedite the time needed to develop optimized LC-UV-MS methods, allowing for a facile, automatic method optimization and reducing the amount of manual work involved in developing new methods during drug development.

Transcriptional and secretome analysis of Rasamsonia emersonii lytic polysaccharide mono-oxygenases.

The current study is the first to describe the temporal and differential transcriptional expression of two lytic polysaccharide monooxygenase (LPMO) genes of Rasamsonia emersonii in response to various carbon sources. The mass spectrometry based secretome analysis of carbohydrate active enzymes (CAZymes) expression in response to different carbon sources showed varying levels of LPMOs (AA9), AA3, AA7, catalase, and superoxide dismutase enzymes pointing toward the redox-interplay between the LPMOs and auxiliary enzymes. Moreover, it was observed that cello-oligosaccharides have a negative impact on the expression of LPMOs, which has not been highlighted in previous reports. The LPMO1 (30 kDa) and LPMO2 (47 kDa), cloned and expressed in Pichia pastoris, were catalytically active with (kcat/Km) of 6.6×10-2 mg-1 ml min-1 and 1.8×10-2 mg-1 ml min-1 against Avicel, respectively. The mass spectrometry of hydrolysis products of Avicel/carboxy methyl cellulose (CMC) showed presence of C1/C4 oxidized oligosaccharides indicating them to be Type 3 LPMOs. The 3D structural analysis of LPMO1 and LPMO2 revealed distinct arrangements of conserved catalytic residues at their active site. The developed enzyme cocktails consisting of cellulase from R. emersonii mutant M36 supplemented with recombinant LPMO1/LPMO2 resulted in significantly enhanced saccharification of steam/acid pretreated unwashed rice straw slurry from PRAJ industries (Pune, India). The current work indicates that LPMO1 and LPMO2 are catalytically efficient and have a high degree of thermostability, emphasizing their usefulness in improving benchmark enzyme cocktail performance. KEY POINTS: • Mass spectrometry depicts subtle interactions between LPMOs and auxiliary enzymes. • Cello-oligosaccharides strongly downregulated the LPMO1 expression. • Developed LPMO cocktails showed superior hydrolysis in comparison to CellicCTec3.

The Multienzyme Complex Nature of Dehydroepiandrosterone Sulfate Biosynthesis.

Dehydroepiandrosterone (DHEA), a precursor of steroid sex hormones, is synthesized by steroid 17-alpha-hydroxylase/17,20-lyase (CYP17A1) with the participation of microsomal cytochrome b5 (CYB5A) and cytochrome P450 reductase (CPR), followed by sulfation by two cytosolic sulfotransferases, SULT1E1 and SULT2A1, for storage and transport to tissues in which its synthesis is not available. The involvement of CYP17A1 and SULTs in these successive reactions led us to consider the possible interaction of SULTs with DHEA-producing CYP17A1 and its redox partners. Text mining analysis, protein-protein network analysis, and gene co-expression analysis were performed to determine the relationships between SULTs and microsomal CYP isoforms. For the first time, using surface plasmon resonance, we detected interactions between CYP17A1 and SULT2A1 or SULT1E1. SULTs also interacted with CYB5A and CPR. The interaction parameters of SULT2A1/CYP17A1 and SULT2A1/CYB5A complexes seemed to be modulated by 3'-phosphoadenosine-5'-phosphosulfate (PAPS). Affinity purification, combined with mass spectrometry (AP-MS), allowed us to identify a spectrum of SULT1E1 potential protein partners, including CYB5A. We showed that the enzymatic activity of SULTs increased in the presence of only CYP17A1 or CYP17A1 and CYB5A mixture. The structures of CYP17A1/SULT1E1 and CYB5A/SULT1E1 complexes were predicted. Our data provide novel fundamental information about the organization of microsomal CYP-dependent macromolecular complexes.

An engineered bacterial therapeutic lowers urinary oxalate in preclinical models and in silico simulations of enteric hyperoxaluria.

Enteric hyperoxaluria (EH) is a metabolic disease caused by excessive absorption of dietary oxalate leading to the formation of chronic kidney stones and kidney failure. There are no approved pharmaceutical treatments for EH. SYNB8802 is an engineered bacterial therapeutic designed to consume oxalate in the gut and lower urinary oxalate as a potential treatment for EH. Oral administration of SYNB8802 leads to significantly decreased urinary oxalate excretion in healthy mice and non-human primates, demonstrating the strain's ability to consume oxalate in vivo. A mathematical modeling framework was constructed that combines in vitro and in vivo preclinical data to predict the effects of SYNB8802 administration on urinary oxalate excretion in humans. Simulations of SYNB8802 administration predict a clinically meaningful lowering of urinary oxalate excretion in healthy volunteers and EH patients. Together, these findings suggest that SYNB8802 is a promising treatment for EH.

Critical challenges and advances in recombinant adeno-associated virus (rAAV) biomanufacturing.

Gene therapy is a promising therapeutic approach for genetic and acquired diseases nowadays. Among DNA delivery vectors, recombinant adeno-associated virus (rAAV) is one of the most effective and safest vectors used in commercial drugs and clinical trials. However, the current yield of rAAV biomanufacturing lags behind the necessary dosages for clinical and commercial use, which embodies a concentrated reflection of low productivity of rAAV from host cells, difficult scalability of the rAAV-producing bioprocess, and high levels of impurities materialized during production. Those issues directly impact the price of gene therapy medicine in the market, limiting most patients' access to gene therapy. In this context, the current practices and several critical challenges associated with rAAV gene therapy bioprocesses are reviewed, followed by a discussion of recent advances in rAAV-mediated gene therapy and other therapeutic biological fields that could improve biomanufacturing if these advances are integrated effectively into the current systems. This review aims to provide the current state-of-the-art technology and perspectives to enhance the productivity of rAAV while reducing impurities during production of rAAV.

Digitalizing the TIM-1 Model using Computational Approaches-Part One: TIM-1 Data Explorer.

The TIM-1 gastrointestinal model is one of the most advanced in vitro systems currently available for biorelevant dissolution testing. This technology, the initial version of which was developed nearly 30 years ago and has been subject to a number of significant updates over this period, simulates the dynamic environment of the human gastrointestinal tract, including pH, transfer times, secretion of bile, enzymes, and electrolytes. In the pharmaceutical industry, the TIM-1 system is used to support drug product design and provide a biopredictive assessment of drug product performance. Typically, the bioaccessibility data sets generated by TIM-1 experiments are used to qualitatively compare formulation performance, and the use of bioaccessibility data as inputs for physiologically based pharmacokinetic (PBPK) modeling for quantitative predictions is limited. To expand the utility of the TIM-1 model beyond standard bioaccessibility measurements (which define the fraction available for absorption), we have developed a computational tool, TIM-1 Data Explorer, to describe the fluid and mass balance within the TIM-1 system. The use of this tool allows a detailed inspection and in-depth interpretation of the experimental data. In addition to mass balance calculation, this model also can be used to describe the critical processes a drug substance would undergo during a TIM-1 experiment, such as dissolution, precipitation on transfer from the stomach to duodenum, and redissolution. The TIM-1 Data Explorer was validated in two case studies. In the first case study with paracetamol, we have shown the ability of the simulator to adequately describe mass transfer events within the TIM-1 system, and in the second study with a weakly basic in-house compound, PF-07059013, the TIM-1 Data Explorer was successfully used to describe dissolution and precipitation processes.

Digitalizing the TIM-1 Model Using Computational Approaches─Part Two: Digital TIM-1 Model in GastroPlus.

A TIM-1 model is an in vitro gastrointestinal (GI) simulator considering crucial physiological parameters that will affect the in vivo drug release process. The outcome of these experiments can indicate the critical bioavailability attributes (CBAs) that will impact the fraction absorbed in vivo. The model is widely used in the nonclinical stage of drug product development to assess the bioaccessible fraction of drugs for numerous candidate formulations. In this work, we developed a digital TIM-1 model in the GastroPlus platform. In a first step, we performed validation experiments to assess the luminal concentrations and bioaccessible fractions for two marker compounds. The digital TIM-1 was able to adequately reflect the luminal concentrations and bioaccessible fractions of these markers under different prandial conditions, confirming the appropriate integration of mass transfer in the TIM-1 model. In a second set of experiments, a case example with PF-07059013 was performed, where luminal concentrations and bioaccessible fractions were predicted for 200 and 1000 mg doses under fasted and achlorhydric conditions. Experimental and simulated data pointed out that the achlorhydric effect was more pronounced at the 1000 mg dose, showing a solubility-limited dissolution and, consequently, decreased bioaccessible fraction. Toward future applications, the digital TIM-1 model will be thoroughly applied to explore a link between in vitro and in vivo outcomes based on more case examples with model compounds with the access of TIM-1 and plasma data. Ideally, this digital TIM-1 can be directly used in GastroPlus to explore an in vitro-in vivo correlation (IVIVC) between the fraction dissolved (digital TIM-1 settings) and the fraction absorbed (human PBPK settings).

Dynamic Changes in Gastrointestinal Fluid Characteristics after Food Ingestion Are Important for Quantitatively Predicting the In Vivo Performance of Oral Solid Dosage Forms in Humans in the Fed State.

The aim of this study was to develop a simulation model to predict the in vivo performance of solid oral dosage forms in humans in the fed state. We focused on investigating the effect of dynamic changes in gastrointestinal (GI) fluid characteristics in the fed state on the in vivo performance of solid dosage forms. We used six solid dosage forms containing weak base drugs as model formulations, two with positive food effects in humans, two with negative food effects, and two which are not affected by food ingestion. These model drug formulations were used to perform biorelevant dissolution tests in the stomach and small intestine under both prandial states. The in vitro properties of the drug products obtained from these tests were then coupled with in silico models (fasted or fed) to predict food effects in humans. We successfully incorporated the dynamic changes in GI fluid characteristics and their effects on the in vivo dissolution of drugs into the prediction model for the fed state. This newly designed physiologically based biopharmaceutics modeling approach provided the precise and quantitative prediction of food effects (i.e., changes in Cmax and AUC after food ingestion) in humans while considering the dynamic changes in fluid characteristics in the fed state.

Forward Genetics-Based Approaches to Understanding the Systems Biology and Molecular Mechanisms of Epilepsy.

Epilepsy is a highly prevalent, severely debilitating neurological disorder characterized by seizures and neuronal hyperactivity due to an imbalanced neurotransmission. As genetic factors play a key role in epilepsy and its treatment, various genetic and genomic technologies continue to dissect the genetic causes of this disorder. However, the exact pathogenesis of epilepsy is not fully understood, necessitating further translational studies of this condition. Here, we applied a computational in silico approach to generate a comprehensive network of molecular pathways involved in epilepsy, based on known human candidate epilepsy genes and their established molecular interactors. Clustering the resulting network identified potential key interactors that may contribute to the development of epilepsy, and revealed functional molecular pathways associated with this disorder, including those related to neuronal hyperactivity, cytoskeletal and mitochondrial function, and metabolism. While traditional antiepileptic drugs often target single mechanisms associated with epilepsy, recent studies suggest targeting downstream pathways as an alternative efficient strategy. However, many potential downstream pathways have not yet been considered as promising targets for antiepileptic treatment. Our study calls for further research into the complexity of molecular mechanisms underlying epilepsy, aiming to develop more effective treatments targeting novel putative downstream pathways of this disorder.

In Vivo and In Silico Analgesic Activity of Ficus populifolia Extract Containing 2-O-ß-D-(3',4',6'-Tri-acetyl)-glucopyranosyl-3-methyl Pentanoic Acid.

Natural product-based structural templates have immensely shaped small molecule drug discovery, and new biogenic natural products have randomly provided the leads and molecular targets in anti-analgesic activity spheres. Pain relief achieved through opiates and non-steroidal anti-inflammatory drugs (NSAIDs) has been under constant scrutiny owing to their tolerance, dependency, and other organs toxicities and tissue damage, including harm to the gastrointestinal tract (GIT) and renal tissues. A new, 3',4',6'-triacetylated-glucoside, 2-O-ß-D-(3',4',6'-tri-acetyl)-glucopyranosyl-3-methyl pentanoic acid was obtained from Ficus populifolia, and characterized through a detailed NMR spectroscopic analysis, i.e., 1H-NMR, 13C-DEPT-135, and the 2D nuclear magnetic resonance (NMR) correlations. The product was in silico investigated for its analgesic prowess, COX-2 binding feasibility and scores, drug likeliness, ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, possible biosystem's toxicity using the Discovery Studio®, and other molecular studies computational software programs. The glycosidic product showed strong potential as an analgesic agent. However, an in vivo evaluation, though at strong levels of pain-relieving action, was estimated on the compound's extract owing to the quantity and yield issues of the glycosidic product. Nonetheless, the F. populifolia extract showed the analgesic potency in eight-week-old male mice on day seven of the administration of the extract's dose in acetic acid-induced writhing and hot-plate methods. Acetic acid-induced abdominal writhing for all the treated groups decreased significantly (p < 0.0001), as compared to the control group (n = 6) by 62.9%, 67.9%, and 70.9% of a dose of 100 mg/kg (n = 6), 200 mg/kg (n = 6), and 400 mg/kg (n = 6), respectively. Similarly, using the analgesia meter, the reaction time to pain sensation increased significantly (p < 0.0001), as compared to the control (n = 6). The findings indicated peripheral and central-nervous-system-mediated analgesic action of the product obtained from the corresponding extract.

A Comparative Evaluation of Desoximetasone Cream and Ointment Formulations Using Experiments and In Silico Modeling.

The administration of therapeutic drugs through dermal routes, such as creams and ointments, has emerged as an increasingly popular alternative to traditional delivery methods, such as tablets and injections. In the context of drug development, it is crucial to identify the optimal doses and delivery routes that ensure successful outcomes. Physiologically based pharmacokinetic (PBPK) models have been proposed to simulate drug delivery and optimize drug formulations, but the calibration of these models is challenging due to the multitude of variables involved and limited experimental data. One significant research gap that this article addresses is the need for more efficient and accurate methods for calibrating PBPK models for dermal drug delivery. This manuscript presents a novel approach and an integrated dermal drug delivery model to address this gap that leverages virtual in vitro release (IVRT) and permeation (IVPT) testing data to optimize mechanistic models. The proposed approach was demonstrated through a study involving Desoximetasone cream and ointment formulations, where the release kinetics and permeation profiles of Desoximetasone were determined experimentally, and a computational model was created to simulate the results. The experimental studies showed that, even though the cumulative permeation of Desoximetasone at the end of the permeation study was comparable, there was a significant difference seen in the lag time in the permeation of Desoximetasone between the cream and ointment. Additionally, there was a significant difference seen in the amount of Desoximetasone permeated through human cadaver skin at early time points when the cream and ointment were compared. The computational model was optimized and validated, suggesting that this approach has the potential to bridge the existing research gap by improving the accuracy and efficiency of drug development processes. The model results show a good fit between the experimental data and model predictions. During the model optimization process, it became evident that there was variability in both the permeability and the partition coefficient within the stratum corneum. This variability had a significant and noteworthy influence on the overall performance of the model, especially when it came to its capacity to differentiate between cream and ointment formulations. Leveraging virtual models significantly aids the comprehension of drug release and permeation, mitigating the demanding data requirements. The use of virtual IVRT and IVPT data can accelerate the calibration of PBPK models, streamline the selection of the appropriate doses, and optimize drug delivery. Moreover, this novel approach could potentially reduce the time and resources involved in drug development, thus making it more cost-effective and efficient.

In silico signaling modeling to understand cancer pathways and treatment responses.

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

Importance of Gastric Secretion and the Rapid Gastric Emptying of Ingested Water along the Lesser Curvature ("Magenstraße") in Predicting the In Vivo Performance of Liquid Oral Dosage Forms in the Fed State Using a Modeling and Simulation.

The objective of the present study was to develop an in silico model of the stomach for predicting oral drug absorption in fed humans. We focused on a model capable of simulating dynamic fluid volume changes and included a simulated Magenstraße "stomach road," a route along the lesser curvature that often carries fluids rapidly to assess the gastric emptying of drugs. Two types of model liquid drug formulations, liquid-filled soft gelatin capsules (enzalutamide, cyclosporine, and nifedipine) and oral solutions (levofloxacin and fenfluramine), were used. An in silico model was assembled, and simulations were performed using Stella Professional software. The secretion rate of the gastric juice induced by food ingestion was assessed along with the gastric emptying of the ingested water via the Magenstraße in the fed state. The model for the fed state successfully described the in vivo performance of the model drug formulations. These results clearly indicate the importance of including gastric secretion and the kinetics of Magenstraße when predicting the in vivo performance of dosage forms using an in silico modeling and simulation of fed humans. This simulation model should be further optimized to allow for the different physiological mechanisms following the ingestion of different types of meals, as well as modifications for interindividual and intraindividual variabilities in gastrointestinal physiology in the fed state in the future.