Correlations between components of the immune system

Background

No evidence of the possibility of altering a constituent of the immune system without directly affecting one of its associated components has yet been shown.

Methods

A schematic model was developed in which two triggers, fasting and splenectomy, were studied for their ability to affect the expression of cell membrane epitopes and the cytokine secretion of out-of-body autogeneic and syngeneic lymphocytes.

Results

The effect of fasting and/or splenectomy on promoting correlations between immune systems was studied by determining the alterations in expressions of cell membrane epitopes and in cytokine secretion by out-of-body autogeneic and syngeneic lymphocytes. The effect of fasting as a trigger decreased expression of CD8 and CD25 and increased TNFα levels. The effect of splenectomy as a trigger was investigated in non-fasting mice by comparing splenectomized and non-splenectomized mice. An increase in the CD8 expression and in TNFα, IFNg, and IL10 secretion was noted. The effect of splenectomy as a trigger in fasting mice was determined by comparing splenectomized and non-splenectomized mice. Splenectomy significantly affected the expression of CD25 and CD4 CD25 and on secretion of TNFα, IFNg, and IL10. To determine the effect of keeping the cells in an out-of-body location on the expression of lymphocyte epitopes, tubes kept on top of the cages of the fasting mice were compared with tubes kept on top of empty cages. The results showed a significant change in the CD8 expression was noted. To determine the effect of keeping cells in an out-of-body location on cytokine secretion, tubes kept on cages were tested for cytokine levels significant decrease was noted in the secretion of TNFα and IFNg.

Conclusions

The study showed that a mouse could affect cells at a distance and alter the expression of surface markers and cytokine secretion following two types of triggers: fasting and/or splenectomy. The data characterized a system for the induction of correlations between two's immune system components without a transfer of mediators. It suggests that an out-of-body correlation can be induced between two components of the immune system.

Special Issue "Computer-Aided Drug Discovery and Treatment"

This Special Issue aims to highlight some of the latest developments in drug discovery [...].

Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stable vs. unstable PA ; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stable vs. unstable PA ; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stable vs. unstable PA ; P=0.02; adjusted P=0.04) and improved short-term memory (score change: -0.28 vs. +0.29, stable vs. unstable PA ; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them.IMPORTANCE SECTIONThe paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.Key messagesAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents.We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

Serum Levels of Adropin Improve the Predictability of MELD and Child-Pugh Score in Cirrhosis: Results of Proof-of-Concept Clinical Trial

Adropin is a peptide that was suggested to have a role in cirrhosis. The present study aimed to determine the ability to use serum adropin levels to improve their prediction accuracy as an adjunct to the current scores. In a single-center, proof-of-concept study, serum adropin levels were determined in thirty-three cirrhotic patients. The data were analyzed in correlation with Child-Pugh and MELD-Na scores, laboratory parameters, and mortality. Adropin levels were higher among cirrhotic patients that died within 180 days (1,325.7 ng/dL vs. 870.3 ng/dL, p = 0.024) and inversely correlated to the time until death (r ² = 0.74). The correlation of adropin serum levels with mortality was better than MELD or Child-Pough scores (r ² = 0.32 and 0.38, respectively). Higher adropin levels correlated with creatinine (r ² = 0.79. p < 0.01). Patients with diabetes mellitus and cardiovascular diseases had elevated adropin levels. Integrating adropin levels with the Child-Pugh and MELD scores improved their correlation with the time of death (correlation coefficient: 0.91 vs. 0.38 and 0.67 vs. 0.32). The data of this feasibility study suggest that combining serum adropin with the Child-Pugh score and MELD-Na score improves the prediction of mortality in cirrhosis and can serve as a measure for assessing kidney dysfunction in these patients.

Increased hepatic Akt phosphorylation alleviated glucose intolerance and improved liver function in leptin-deficient mice

Aim of the study

Akt is involved in upregulating the insulin-signaling pathways essential for maintaining glucose metabolism. Glycosphingolipids are involved in the pathogenesis of glucose intolerance and associated target organ injury. On the other hand, oral administration of b-glucosylceramide (GC) has been shown to alleviate insulin resistance. The present study aimed to determine the effects of oral administration of insulin and GC, separately and in combination, on Akt expression and the subsequent effect on metabolic syndrome characteristics in leptin-deficient mice.

Material and methods

Four groups of leptin-deficient ob/ob mice were orally administered for four weeks: vehicle, GC, short-acting insulin, and GC combined with insulin. Mice were followed for hepatic Akt expression and changes in tumor necrosis factor a (TNF-a) level, hyperlipidemia, and liver damage.

Results

In mice that received insulin or GC, particularly those that received both, the liver phosphorylation of Akt was significantly increased compared to those that received only vehicle. Serum TNF-a levels decreased in insulin-treated mice. These effects were associated with alleviating glucose intolerance and hyperlipidemia, as manifested by a significant glucose tolerance test improvement and reductions in serum triglyceride and cholesterol levels. Significant liver damage alleviation was noted by liver enzyme reductions in all treated groups, along with liver steatosis in the insulin-treated mice.

Conclusions

These data established the potential use of oral insulin administration with glycosphingolipids to alleviate glucose intolerance and associated liver damage and hyperlipidemia via increased Akt expression in the liver. The data support targeting Akt as a potent therapeutic target for metabolic syndrome.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

INTRODUCTION

Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens.

METHODS

Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function.

RESULTS

The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations.

SUMMARY

The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.

Making use of noise in biological systems

Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

Aging is a complex biological process with multifactorial nature underlined by genetic, environmental, and social factors. In the present paper, we review several mechanisms of aging and the pre-clinically and clinically studied anti-aging therapies. Variability characterizes biological processes from the genome to cellular organelles, biochemical processes, and whole organs' function. Aging is associated with alterations in the degrees of variability and complexity of systems. The constrained disorder principle defines living organisms based on their inherent disorder within arbitrary boundaries and defines aging as having a lower variability or moving outside the boundaries of variability. We focus on associations between variability and hallmarks of aging and discuss the roles of disorder and variability of systems in the pathogenesis of aging. The paper presents the concept of implementing the constrained disease principle-based second-generation artificial intelligence systems for improving anti-aging modalities. The platform uses constrained noise to enhance systems' efficiency and slow the aging process. Described is the potential use of second-generation artificial intelligence systems in patients with chronic disease and its implications for the aged population.

Codivir suppresses SARS-Cov-2 viral replication and stabilizes clinical outcome: In vitro and Phase I clinical trial results

BACKGROUND

Treatment of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) remains a significant challenge in the face of increased worldwide morbidity and mortality. The acute illness caused by SARS-CoV-2 is initiated by a viral phase, followed by an inflammatory phase. Numerous anti-inflammatory and anti-viral therapies, with a relatively minor clinical effect, have been applied. Developing a safe and efficient direct anti-viral treatment is essential as it can block disease progression before significant complications ensue and potentially prevent transmission.

AIM

The present phase 1 study aimed to determine the safety of Codivir, a newly developed anti-viral agent, and to preliminarily assess its anti-viral activity in patients infected by COVID-19.

METHODS

In vitro studies were conducted to determine the direct anti-viral effect of Codivir using an immunofluorescence-based assay and to assess its cytotoxic effect by tetrazolium assay (MTT). In a phase I clinical trial, Codivir was administered for ten days in 12 patients who were followed for its safety. Patients were followed for clinical manifestations during administration. Sequential nasal viral PCR titers (Cycle Threshold, CT) were determined preceding and during treatment.

RESULTS

In vitro, Codivir showed activity against SARS-CoV-2 with 90% viral replication suppression and minimal cytotoxicity. The anti-viral activity was demonstrated at the early stages of infection, post-entry of the virus in the cell. Codivir was safe in all 12 patients in phase I clinical trial and significantly suppressed viral replication in 5/7 fully assessed patients, with an anti-viral effect noted as early as three days.

SUMMARY

The present study's data support the safety of Codivir administration in humans and suggest its significant anti-COVID-19 effect. These results support the testing of the drug in more extensive controlled trials in patients with SARS-CoV-2.

Abstract PO001: Preclinical activity of glypican-3 (GPC3) and NKp46 directed FLEX-NK™ engager antibody (CYT-303) in combination with iPSC derived natural killer cells (iNKs) or peripheral blood (PB) NK cells in hepatocellular carcinoma (HCC)

BACKGROUND: GPC3 is highly expressed in HCC while it is hardly expressed in adult normal tissues except placenta. Given the presence of dysfunctional NK cells in the HCC tumor microenvironment that contribute to poor anti-tumor immune responses, we sought to develop an iNK cell therapy in combination with GPC3 targeted NK cell engagers (NKE). FLEX-NK™ is a proprietary tetravalent IgG1-like multifunctional antibody NKE platform with a novel FLEX-linker to allow for simultaneous binding to different antigens on different cells. CYT-303 was constructed to engage NK cells via the activation receptor NKp46 and to target cancer cells via GPC3. We hypothesized that CYT-303 may be active in the tumor microenvironment where there are sufficient number and function of endogenous NK cells, but that this activity may be augmented by fresh functional NK cells. iNK cells derived from iPSC, a uniform starting material with unlimited self-renewal capabilities, can be expanded to produce a universal off-the-shelf allogeneic therapy. We investigated the combination of CYT-303 to enhance tumor specificity and potency of iNKs. METHODS: CYT-303 was expressed in CHOZEN cells and purified by step column chromatography. iNK’s were differentiated from iPSC’s and expanded using irradiated feeder cells. PB-NK cells were purified from healthy donor blood. iNK and PB-NK cytotoxicity against Hep3B tumors was evaluated in the presence of CYT-303 or isotype control antibodies at a fixed E/T ratio. CYT-303 pharmacokinetics in mice was evaluated following a single intravenous injection. Anti-tumor efficacy of the iNK combination with CYT-303 was evaluated in NSG-IL15 mice bearing subcutaneous Hep3B tumors following intra-tumoral injection of iNKs and intravenous dosing with CYT-303. Blood alpha-fetoprotein (AFP) levels were assessed by immunoassay. Cytokine release assessments for CYT-303 was evaluated in an in-vitro PBMC activation assay. RESULTS: iNK cells express high levels of multiple activation receptors including NKp46, but low levels of CD16. Despite low expression of CD16, natural cytotoxicity of iNK’s against Hep3B tumors was higher than with fresh PB-NK and CYT-303 redirected killing of Hep3B tumors was comparable to PB-NK’s. The CYT-303 pharmacokinetics study in mice showed a terminal half-life of 17 hrs. In a Hep3B tumor model in NSG-hIL-15 mice, the combination of CYT-303 and iNK’s showed significantly greater tumor growth inhibition compared to iNK’s alone plus an IgG1 isotype control. Blood AFP levels decreased in the CYT-303 plus iNK combination compared to iNK’s alone. Cytokine release assessment of CYT-303 in the human PBMC assay showed no evidence of cytokine release while high levels of cytokine release was observed with anti-CD28 (TGN1412) and CD3 antibody controls. CONCLUSIONS: The combination of NK cell engager CYT-303 and iNK cells is active in preclinical models of HCC with low levels of cytokine release. These data support further development of CYT-303 and iNKs to engage and empower NK cells as an off the shelf therapeutic approach in HCC.

Citation Format: Antonio Arulanandam, Liang Lin, Hao-Ming Chang, Harish Potu, Vishal Khairnar, David Zou, Melissa Triggiano, Nejmi Dilmac, Yinan Yang, Shira Kahlon, Stanley Frankel, Jean Kadouche, Daniel Teper, Ofer Mandelboim, Yoav Hershkovitz, Yaron Ilan, Wei Li. Preclinical activity of glypican-3 (GPC3) and NKp46 directed FLEX-NK™ engager antibody (CYT-303) in combination with iPSC derived natural killer cells (iNKs) or peripheral blood (PB) NK cells in hepatocellular carcinoma (HCC) [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PO001.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Personalized-Inherent Variability in a Time-Dependent Immune Response: A Look into the Fifth Dimension in Biology

Introduction

Individualized response to the immune triggers influences the course of immune-mediated diseases and the response to immunotherapies. Both inter- and intra-subject variations occur in time-dependent dynamics of biological systems. The present study aimed to establish a model for inherent personalized-time-dependent variability in response to immune triggers.

Methods

Male C57BL/6 mice were administered concanavalin A (ConA) and followed every 2 h for 10 h and at 24 h for serum alanine aminotransferase (ALT) levels.

Results

A marked intragroup variability was noted for both the timing of the effect of ConA, the magnitude of the increase in ALT levels, and the time to peak. While in some mice, a peak level was achieved, whereas a continuous increase in liver damage was noted in others. Four mice died at different time points during the study irrespective of their liver damage, further supporting the individualized-based response to the trigger.

Conclusions

This feasibility study established a model for determining the personalized-inherent variability in a time-dependent response to the immune triggers. These results highlight the importance of considering both the time and the wide range of individualized variability in immune responses while designing personalized-based immunotherapies.

The role of bacterial translocation in sepsis: a new target for therapy

Sepsis is a leading cause of death in critically ill patients, primarily due to multiple organ failures. It is associated with a systemic inflammatory response that plays a role in the pathogenesis of the disease. Intestinal barrier dysfunction and bacterial translocation (BT) play pivotal roles in the pathogenesis of sepsis and associated organ failure. In this review, we describe recent advances in understanding the mechanisms by which the gut microbiome and BT contribute to the pathogenesis of sepsis. We also discuss several potential treatment modalities that target the microbiome as therapeutic tools for patients with sepsis.

Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects

Opioids remain an essential part of the treatment of chronic pain. However, their use and increasing rates of misuse are associated with high morbidity and mortality. The development of tolerance to opioids and analgesics further complicates dosing and the need to reduce side effects. First-generation digital systems were developed to improve analgesics but are not always capable of making clinically relevant associations and do not necessarily lead to better clinical efficacy. A lack of improved clinical outcomes makes these systems less applicable for adoption by clinicians and patients. There is a need to enhance the therapeutic regimens of opioids. In the present paper, we present the use of a digital analgesic that consists of an analgesic administered under the control of a second-generation artificial intelligence system. Second-generation systems focus on improved patient outcomes measured based on clinical response and reduced side effects in a single subject. The algorithm regulates the administration of analgesics in a personalized manner. The digital analgesic provides advantages for both users and providers. The system enables dose optimization, improving effectiveness, and minimizing side effects while increasing adherence to beneficial therapeutic regimens. The algorithm improves the clinicians’ experience and assists them in managing chronic pain. The system reduces the financial burden on healthcare providers by lowering opioid-related morbidity and provides a market disruptor for pharma companies.

Steroid-mediated liver steatosis is CD1d-dependent, while steroid-induced liver necrosis, inflammation, and metabolic changes are CD1d-independent

Introduction

Glucocorticoids contribute to the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Natural killer T cells play a role in the pathogenesis of NAFLD and response to steroids. The present study aimed to determine the role of CD1d in steroid-mediated metabolic derangement and the steroid-protective effect of glycosphingolipids.

Methods

Ten groups of mice were studied. Steroids were orally administered to C57BL/6 mice to assess the therapeutic effect of β-glucosylceramide (GC) on the development of steroid-mediated liver damage and metabolic derangements. The role of CD1d in the pathogenesis of steroid-induced liver damage and in mediating the hepatoprotective effect of GC was studied in CD1d^−/− mice.

Results

A model of oral administration of steroids was established, resulting in insulin resistance, hyperinsulinemia, hypertriglyceridemia, liver steatosis, and hepatocellular injury. Steroid administration to CD1d^−/− mice was associated with hyperglycemia and hypertriglyceridemia. However, CD1d^−/− mice did not manifest marked steroid-induced steatosis. GC treatment alleviated steroid-associated metabolic derangements and liver injury independent of CD1d expression.

Conclusion

A steroid-mediated model of NAFLD and metabolic derangements was established in which steroid-mediated steatosis was CD1d-dependent while steroid-induced liver necrosis, inflammation, and metabolic changes were CD1d-independent, which may support a dichotomy between steatosis and steatohepatitis in NAFLD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02242-9.

A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review

The plateau effect in training is a significant obstacle for professional athletes and average subjects. It evolves from both the muscle-nerve-axis-associated performance and various cardiorespiratory parameters. Compensatory adaptation mechanisms contribute to a lack of continuous improvement with most exercise regimens. Attempts to overcome this plateau in exercise have been only partially successful, and it remains a significant unmet need in both healthy subjects and those suffering from chronic neuromuscular, cardiopulmonary, and metabolic diseases. Variability patterns characterize many biological processes, from cellular to organ levels. The present review discusses the significant obstacles in overcoming the plateau in training and establishes a platform to implement subject-tailored variability patterns to prevent and overcome this plateau in muscle and cardiorespiratory performance.

Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response

Legalized use of cannabis products and the rising interest in their therapeutic benefits have opened up new opportunities for therapy and marketing. However, the marked variability in formulations, administration modes, therapeutic regimens, and inter- and intra-subject responses make the standardization of medical cannabis-based regimens difficult. Legalization has made the cannabis market highly competitive and lowered the revenue margins. This study reviews some of the challenges in medical cannabis use and difficulties in standardizing its therapeutic regimens that hinder maximizing its beneficial effects. The development of tolerance toward cannabis and low adherence to chronic administration further impair its long-term beneficial effects. Digital medical cannabis is a cannabis product controlled by a second-generation artificial intelligence (AI) system that improves patient responses by increasing adherence and dealing with tolerance. Second-generation AI systems focus on a single patient's outcome and deal with the inter- and intra-subject variability in responses. The use of digital medical cannabis is expected to improve product standardization, maximize therapeutic benefits, reduce health care costs, and increase the revenue of companies. Digital medical cannabis offers several market differentiators for cannabis companies. This study presents a model for promoting the use of digital medical cannabis and presents its advantages for patients, clinicians, health care authorities, insurance companies, and cannabis manufacturers. Ongoing trials and real-world data on the use of these systems further support the use of digital medical cannabis for improved global health.

A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage

Coronavirus disease 2019 (COVID-19), which is a respiratory illness associated with high mortality, has been classified as a pandemic. The major obstacles for the clinicians to contain the disease are limited information availability, difficulty in disease diagnosis, predicting disease prognosis, and lack of disease monitoring tools. Additionally, the lack of valid therapies has further contributed to the difficulties in containing the pandemic. Recent studies have reported that the dysregulation of the immune system leads to an ineffective antiviral response and promotes pathological immune response, which manifests as ARDS, myocarditis, and hepatitis. In this study, a novel platform has been described for disseminating information to physicians for the diagnosis and monitoring of patients with COVID-19. An adjuvant approach using compounds that can potentiate antiviral immune response and mitigate COVID-19-induced immune-mediated target organ damage has been presented. A prolonged beneficial effect is achieved by implementing algorithm-based individualized variability measures in the treatment regimen.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

Augmented antiviral T cell immunity by oral administration of IMM-124E in preclinical models and a phase I/IIa clinical trial: A method for the prevention and treatment of COVID-19

Biological adjuvants that target the gut immune system are being developed for modulating the immune system. Hyperimmune bovine colostrum (HBC), produced by harvesting the bovine colostrum of dairy cows immunized to exogenous antigens, has been shown to modulate the immune responses and alleviate immune‐mediated organ damages. The aim of the present study was to determine the ability of HBC to promote antiviral interferonγ (IFNγ) T cell responses. In a preclinical study, mice were orally administered with HBC for 5 days and tested for the number of T cell clones secreting IFNγ in response to viral antigens of the swine flu, New Caledonia influenza, and cytomegalovirus. In a phase I/IIa clinical trial, five healthy volunteers were treated for 5 days with HBC followed by testing the anti‐coronavirus disease (COVID‐19) immunity. In the preclinical study, oral administration of HBC augmented the number of T cell clones secreting IFNγ in response to viral antigens. In the clinical trial, oral administration of HBC to healthy males significantly increased the number of anti‐COVID‐19 spike protein IFNγ positive T cell clones. Oral administration of HBC provides a novel method for augmenting antiviral responses. Its high‐safety profile makes it ideal for all disease stages and for pre‐emptive therapy among medical personnel and other workers who are at a high risk of exposure to infections. The relatively low cost of HBC is expected to minimize care provider burdens, costs, and enable its global application.

Role of circadian rhythm and autonomic nervous system in liver function: a hypothetical basis for improving the management of hepatic encephalopathy

Hepatic encephalopathy (HE) is a common, incapacitating complication of cirrhosis that affects many patients with cirrhosis. Although several therapies have proven effective in the treatment and prevention of this condition, several patients continue to suffer from covert disease or episodes of relapse. The circadian rhythm has been demonstrated to be pivotal for many body functions, including those of the liver. Here, we explore the impact of circadian rhythm-dependent signaling on the liver and discuss the evidence of its impact on liver pathology and metabolism. We describe the various pathways through which circadian influences are mediated. Finally, we introduce a novel method for improving patient response to drugs aimed at treating HE by utilizing the circadian rhythm. A digital system that introduces a customization-based technique for improving the response to therapies is presented as a hypothetical approach for improving the effectiveness of current medications used for the treatment of recurrent and persistent hepatic encephalopathy.

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Heart failure is a major public health problem, which is associated with significant mortality, morbidity, and healthcare expenditures. A substantial amount of the morbidity is attributed to volume overload, for which loop diuretics are a mandatory treatment. However, the variability in response to diuretics and development of diuretic resistance adversely affect the clinical outcomes. Morevoer, there exists a marked intra- and inter-patient variability in response to diuretics that affects the clinical course and related adverse outcomes. In the present article, we review the mechanisms underlying the development of diuretic resistance. The role of the autonomic nervous system and chronobiology in the pathogenesis of congestive heart failure and response to therapy are also discussed. Establishing a novel model for overcoming diuretic resistance is presented based on a patient-tailored variability and chronotherapy-guided machine learning algorithm that comprises clinical, laboratory, and sensor-derived inputs, including inputs from pulmonary artery measurements. Inter- and intra-patient signatures of variabilities, alterations of biological clock, and autonomic nervous system responses are embedded into the algorithm; thus, it may enable a tailored dose regimen in a continuous manner that accommodates the highly dynamic complex system.

Orally administered anti-eotaxin-1 monoclonal antibody is biologically active in the gut and alleviates immune-mediated hepatitis: A novel anti-inflammatory personalized therapeutic approach

Personalized therapies are designed to optimize the safety-to-efficacy ratio by selecting patients with higher response rates based on specific biomarkers. Inflammation plays a vital role in the pathogenesis of non-alcoholic steatohepatitis (NASH), a common liver disorder. Eotaxin-1 plays a role in innate and adaptive immune responses. High eotaxin-1 levels are associated with diabetes and fatty liver disease and, therefore, serves as a biomarker for patient selection. The anti-eotaxin-1 monoclonal antibody is tailored for the personalized therapy of patients with inflammatory conditions due to high levels of eotaxin-1. To evaluate the biological activity and immunomodulatory effect of orally administered anti-eotaxin-1. C57B1/6 mice were treated with either oral or intra-peritoneal anti-eotaxin-1 antibody before induction of immune-mediated hepatitis using an injection of concanavalin A (ConA) and checked for liver injury and eotaxin-1 serum levels. Oral administration of anti-eotaxin-1 alleviated the immune-mediated liver injury. Serum alanine aminotransferase levels decreased to 1807 U/L, compared with 19025 U/L in untreated controls and 3657 U/L in mice treated with parenteral anti-eotaxin-1 (P < 0.005). A trend toward reduced serum eotaxin-1 levels was observed in treated mice, ranging from 594 pg/mL in the controls to 554 and 561 pg/mL in mice treated orally and intraperitoneally (P = 0.08, P = 0.06, respectively). Oral administration of anti-eotaxin-1 antibody shows biological activity in the gut and exerts a systemic immunomodulatory effect to alleviate immune-mediated hepatitis. The data suggest that testing for eotaxin-1 serum levels may enable screening patients with high-eotaxin-1 levels-associated NASH.

Introducing variability in targeting the microtubules: Review of current mechanisms and future directions in colchicine therapy

Microtubules (MTs) are highly dynamic polymers that constitute the cellular cytoskeleton and play a role in multiple cellular functions. Variability characterizes biological systems and is considered a part of the normal function of cells and organs. Variability contributes to cell plasticity and is a mechanism for overcoming errors in cellular level assembly and function, and potentially the whole organ level. Dynamic instability is a feature of biological variability that characterizes the function of MTs. The dynamic behavior of MTs constitutes the basis for multiple biological processes that contribute to cellular plasticity and the timing of cell signaling. Colchicine is a MT‐modifying drug that exerts anti‐inflammatory and anti‐cancer effects. This review discusses some of the functions of colchicine and presents a platform for introducing variability while targeting MTs in intestinal cells, the microbiome, the gut, and the systemic immune system. This platform can be used for implementing novel therapies, improving response to chronic MT‐based therapies, overcoming drug resistance, exerting gut‐based systemic immune responses, and generating patient‐tailored dynamic therapeutic regimens.

Establishment of an Individualized Chronotherapy, Autonomic Nervous System, and Variability-Based Dynamic Platform for Overcoming the Loss of Response to Analgesics

Background: Control of chronic pain and mainly the partial or complete loss of response to analgesics is a major unmet need. Multiple mechanisms underline the development of tolerance to analgesics in general and specifically to opioids. The autonomic nervous system (ANS) plays a role in the development of analgesic tolerance and chronobiology. Objectives: To review the mechanisms associated with the development of nonresponsiveness to analgesics. Study Design: Literature review. Setting: The review is followed by a description of a new method for overcoming resistance and improving the response to analgesics. Methods: Conducted a detailed review of the relevant studies describing the mechanisms that underlie tolerance to pain medications, and the potential roles of the ANS and chronobiology in the development of drug resistance. Results: The autonomic balance is reflected by heart rate variability, an example of a fundamental variability that characterizes biological systems. Chronotherapy, which is based on the circadian rhythm, can improve the efficacy and reduce the toxicity of chronic medications. In this article, we present the establishment of an individualized variability- and chronobiology-based therapy for overcoming the compensatory mechanisms associated with a loss of response to analgesics. We describe the premise of implementing personalized signatures associated with the ANS, and chronobiology, as well as with the pathophysiology of pain for establishing an adaptive model that could improve the efficacy of opioids, in a highly dynamic system. Limitations: The studies presented were selected based on their relevance to the subject. Conclusions: The described variability-based system may ensure prolonged effects of analgesics while reducing the toxicity associated with increasing dosages. Key words: Painkillers, opioids, drug resistance, compensatory mechanisms

Establishment of an Individualized Chronotherapy, Autonomic Nervous System, and Variability-Based Dynamic Platform for Overcoming the Loss of Response to Analgesics

BACKGROUND

Control of chronic pain and mainly the partial or complete loss of response to analgesics is a major unmet need. Multiple mechanisms underline the development of tolerance to analgesics in general and specifically to opioids. The autonomic nervous system (ANS) plays a role in the development of analgesic tolerance and chronobiology.

OBJECTIVES

To review the mechanisms associated with the development of nonresponsiveness to analgesics.

STUDY DESIGN

Literature review.

SETTING

The review is followed by a description of a new method for overcoming resistance and improving the response to analgesics.

METHODS

Conducted a detailed review of the relevant studies describing the mechanisms that underlie tolerance to pain medications, and the potential roles of the ANS and chronobiology in the development of drug resistance.

RESULTS

The autonomic balance is reflected by heart rate variability, an example of a fundamental variability that characterizes biological systems. Chronotherapy, which is based on the circadian rhythm, can improve the efficacy and reduce the toxicity of chronic medications. In this article, we present the establishment of an individualized variability- and chronobiology-based therapy for overcoming the compensatory mechanisms associated with a loss of response to analgesics. We describe the premise of implementing personalized signatures associated with the ANS, and chronobiology, as well as with the pathophysiology of pain for establishing an adaptive model that could improve the efficacy of opioids, in a highly dynamic system.

LIMITATIONS

The studies presented were selected based on their relevance to the subject.

CONCLUSIONS

The described variability-based system may ensure prolonged effects of analgesics while reducing the toxicity associated with increasing dosages.

Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity

Prevention of weight regain following successful weight loss is a major challenge in the treatment of obesity, irrespective of the weight reduction method used. The majority of individuals regain the lost weight over time; thus, achieving long-term sustainability in weight loss remains an unresolved issue. A compensatory adaptation to the weight loss methods occurs in several body organs and partly explains the lack of sustainable effect. Variability is inherent in many biological systems, and patterns of variability constitute a body mechanism that is active at several levels, starting from the genes and cellular pathways through to the whole-organ level. This study aimed to describe a platform that introduces individually tailored variability in vagal nerve stimulation and dietary regimen to ensure prolonged and sustainable weight loss and prevent weight regain. The platform is intended to provide a method that can overcome the body's compensatory adaptation mechanisms while ensuring a prolonged beneficial effect.

Why scientists, academic institutions, and investors fail in bringing more products to the bedside: the Active Compass model for overcoming the innovation paradox

The vast majority of good science and excellent ideas do not translate into products. Many good products that have the potential to assist in diagnosis and therapy do not mature into everyday care. This often becomes a source of frustration for innovators, academic institutions, companies both small and large, and investors. The "innovation paradox" , wherein excellent ideas and good science fail to reach the bedside, is a major challenge. This study presents the Active Compass model as a way to overcome this obstacle. The model is designed to assist projects at early stages by redirecting and reshaping them in a way that increases their chances of reaching the markets. The model is based on the use of next-generation translational research and on creating differentiators at the early stages of development. The proposed model's implementation by innovators, scientists, technology transfer offices, academic institutions, analysts, and investors can help move forward high-potential projects to improve the quality of life and alleviate the burdens of disease.

Adropin as a potential mediator of the metabolic system-autonomic nervous system-chronobiology axis: Implementing a personalized signature-based platform for chronotherapy

Adropin is a peptide hormone, which plays a role in energy homeostasis and controls glucose and fatty acid metabolism. Its levels correlate with changes in carbohydrate-lipid metabolism, metabolic diseases, central nervous system function, endothelial function and cardiovascular disease. Both metabolic pathways and adropin are regulated by the circadian clocks. Here, we review the roles of the autonomic nervous system and circadian rhythms in regulating metabolic pathways and energy homeostasis. The beneficial effects of chronotherapy in various systems are discussed. We suggest a potential role for adropin as a mediator of the metabolic system-autonomic nervous system axis. We discuss the possibility of establishing an individualized adropin and circadian rhythm-based platform for implementing chronotherapy, and variability signatures for improving the efficacy of adropin-based therapies are discussed.

Novel Orally Administered Recombinant Anti-TNF Alpha Fusion Protein for the Treatment of Ulcerative Colitis: Results From a Phase 2a Clinical Trial

BACKGROUND AND OBJECTIVE

OPRX-106 is an orally administered BY2 plant cell-expressing recombinant TNF fusion protein (TNFR). Oral administration of OPRX-106 was shown to be safe and effective in inducing favorable anti-inflammatory immune modulation in humans. The current study was aimed at determining the safety and efficacy of OPRX-106 in patients with ulcerative colitis (UC).

METHODS

Twenty-five patients with active mild-to-moderate UC were enrolled in an open-label trial. Patients were randomized to receive 2 or 8 mg of OPRX-106 administered orally once daily, for 8 weeks. Patients were monitored for safety and efficacy including clinical response or clinical remission, based on the Mayo score. The histopathological improvement in Geboes score, calprotectin level and hs-CRP, and exploratory immune parameters by means of fluorescence-activated cell sorting and cytokine levels were monitored.

RESULTS

Oral administration of OPRX-106 was found to be safe and well tolerated without absorption into the circulation. Out of 24 patients, 18 completed the trial. The analysis of the patients completing treatment demonstrated clinical efficacy as measured by clinical response or remission in 67% and 28%, respectively. Reduction in calprotectin levels and improved Geboes score were noted in the majority of the treated patients. The beneficial clinical effect was associated with an increase in a CD4+CD25+FoxP3 subset of suppressor lymphocytes and a reduction in interleukin 6 and interferon gamma serum levels.

CONCLUSIONS

Oral administration of the nonabsorbable OPRX-106 is safe and effective in mild-to-moderate UC, and not associated with immune suppression, while inducing favorable anti-inflammatory immune modulation.

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

The role of the sphingosine axis in immune regulation: A dichotomy in the anti-inflammatory effects between sphingosine kinase 1 and sphingosine kinase 2-dependent pathways

Background: Sphingosine kinase has been identified as playing a central role in the immune cascade, being a common mediator in the cellular response to a variety of signals. The different effects of sphingosine kinase 1 and 2 (SphK1 and SphK2, respectively) activity have not been completely characterized. Aim: To determine the different roles played by SphK1 and SphK2 in the regulation of immune-mediated disorders. Methods: Nine groups of mice were studied. Concanavalin A (ConA) injection was used to induce immune-mediated hepatitis. Mice were treated with SphK1 inhibitor (termed SphK-I) and SphK2 inhibitor (termed ABC294640), prior to ConA injection, and effects of treatment on liver enzymes, subsets of T lymphocytes, and serum levels of cytokines were observed. Results: While liver enzyme elevation was ameliorated by administration of SphK1 inhibitor, SphK2 inhibitor-treated mice did not show this tendency. A marked decrease in expression of CD25+ T-cells and Foxp+ T-cells was observed in mice treated with a high dose of SphK1 inhibitor. Alleviation of liver damage was associated with a statistically significant reduction of serum IFNγ levels in mice treated with SphK1 inhibitor and not in those treated with SphK2 inhibitor. Conclusions: Early administration of SphK1 inhibitor in a murine model of immune-mediated hepatitis alleviated liver damage and inflammation with a statistically significant reduction in IFN-γ levels. The data support a dichotomy in the anti-inflammatory effects of SphK1 and SphK2, and suggests that isoenzyme-directed therapies can improve the effect of targeting these pathways.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: an algorithm-based method for overcoming resistance to antiviral agents

The ongoing severe acute respiratory syndrome pandemic caused by the novel coronavirus 2 (SARS-CoV-2) is associated with high morbidity and mortality rates, and it has created a pressing global need for effective antiviral therapies against it. COVID-19 disease pathogenesis is characterized by an initial virus-mediated phase, followed by inappropriate hyperactivation of the immune system leading to organ damage. Targeting of the SARS-CoV-2 viral receptors is being explored as a therapeutic option for these patients. In this paper, we summarize several potential receptors associated with the infectivity of SARS-CoV-2 and discuss their association with the immune-mediated inflammatory response. The potential for the development of resistance towards antiviral drugs is also presented. An algorithm-based platform to improve the efficacy of and overcome resistance to viral receptor blockers through the introduction of personalized variability is described. This method is designed to ensure sustained antiviral effectiveness when using SARS-CoV-2 receptor blockers.

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance

Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.

Short-Term Cohousing of Sick with Healthy or Treated Mice Alleviates the Inflammatory Response and Liver Damage

Cohousing of sick with healthy or treated animals is based on the concept of sharing an intestinal ecosystem and coprophagy, the consumption of feces, which includes sharing of the microbiome and of active drug metabolites secreted in the feces or urine. To develop a model for short-term cohousing, enabling the study of the effect of sharing an ecosystem on inflammatory states. To determine the impact of cohousing of sick and healthy mice on the immune-mediated disorders, mice injected with concanavalin A (ConA) were cohoused with healthy or sick mice or with steroid-treated or untreated mice. To determine the effect of cohousing on acetaminophen (APAP)-induced liver damage, APAP-injected mice were cohoused with N-acetyl-cysteine (NAC)-treated or untreated mice. In the ConA-induced immune-mediated hepatitis model, cohousing of sick with healthy mice was associated with the alleviation of liver damage in sick animals. Similarly, a significant decrease in serum ALT was noted in ConA-injected mice kept in the same cage as ConA-injected mice treated with steroids. A trend for reduction in liver enzymes in APAP-injected mice was observed upon cohousing with NAC-treated animals. Cohousing of sick mice with healthy or treated mice ameliorated the immune-mediated inflammatory state induced by ConA and APAP. These models for liver damage can serve as biological systems for determining the effects of alterations in the ecosystem on the immune system.

Antibody response to SARS-Co-V-2, diagnostic and therapeutic implications

The immune response against SARS-CoV-2 is comprised of both cellular and humoral arms. While current diagnostic methods are mainly based on PCR, they suffer from insensitivity. Therefore, antibody-based serological tests are being developed to achieve higher sensitivity and specificity. Current efforts in treating SARS-CoV-2 infection include blocking of viral entry into the host cells, prohibiting viral replication and survival in the host cells, or reducing the exaggerated host immune response. Administration of convalescent plasma containing anti-viral antibodies was proposed to improve the outcome in severe cases. In this paper, we review some of the aspects associated with the development of antibodies against SARS-CoV-2 and their potential use for improved diagnosis and therapy.

The assembly of glycosphingolipid determines their immunomodulatory effect: A novel method for structure-based design of immunotherapy

Structure-activity relationships provide insight into the binding interactions of beta-glycosphingolipids (GSLs) with both the TCR and the CD1d molecules, as well as the subsequent immunologic response of regulatory NKT cells.

AIM

To determine the effects of synthetic GSL structures on their immune modulatory functions.

METHODS

GSLs of various structures were tested in vitro and in an animal model of Concanavalin A (ConA) immune-mediated hepatitis.

RESULTS

In vitro, using SV40 binding to live monkey CV1 cells, the l-threo stereoisomer of C8-β-LacCer inhibits caveolar internalization, reducing viral binding to the cell surface. In vivo, in the ConA model, LR172, which has a saturated C8 chain, and LR178, which has a trans double bond at C-2 in the C8 chain, suppressed the immune-mediated liver inflammation and reduced IFNγ levels in a dose dependent manner. The beneficial effects of LR172 and of LR178 are associated with suppression of liver apoptosis, increased phosphorylated STAT3 expression in the liver, and an increase in the NKT liver/spleen ratio.

SUMMARY

The assembly of GSLs determines their immunomodulatory effect and can serve as a method for structure-based design of immunotherapy.

Overcoming Compensatory Mechanisms toward Chronic Drug Administration to Ensure Long-Term, Sustainable Beneficial Effects

Chronic administration of drugs leads to the activation of compensatory mechanisms that may inhibit some of their activity and induce unwanted toxicity. These mechanisms are an obstacle for maintaining a sustainable effect for many chronic medications. Pathways that adapt to the burden induced by chronic drugs, whether or not related to the underlying disease, can lead to a partial or complete loss of effect. Variability characterizes many biological systems and manifests itself as large intra- and inter-individual differences in the response to drugs. Circadian rhythm-based chronotherapy is further associated with variability in responses noted among patients. This paper reviews current knowledge regarding the loss of effect of chronic medications and the range of variabilities that have been described in responses and loss of responses. Establishment of a personalized platform for overcoming these prohibitive mechanisms is presented as a model for ensuring long-term sustained medication effects. This novel platform implements personalized variability signatures and individualized circadian rhythms for preventing and opposing the prohibitive effect of the compensatory mechanisms induced by chronic drug administration.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

The role of the sphingolipid pathway in liver fibrosis: an emerging new potential target for novel therapies

Sphingolipids (SL) are a family of bioactive lipids and a major cellular membrane structural component. SLs include three main compounds: ceramide (Cer), sphingosine (Sp), and sphingosine-1-phosphate (S-1P), all of which have emerging roles in biological functions in cells, especially in the liver. They are under investigation in various liver diseases, including cirrhosis and end-stage liver disease. In this review, we provide an overview on the role of SLs in liver pathobiology and focus on their potential role in the development of hepatic fibrosis. We describe recent evidence and suggest SLs are a promising potential therapeutic target for the treatment of liver disease and fibrosis.