scholarly journals A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

2021 ◽  
Author(s):  
Rohit Rao ◽  
Cynthia J. Musante ◽  
Richard Allen
Keyword(s):  
Immune Response ◽  
Viral Load ◽  
Symptom Onset ◽  
In Silico ◽  
Clinical Trial Design ◽  
Baseline Viral Load ◽  
Optimal Timing ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Wide Range

AbstractA quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.

scholarly journals Verifying and Validating Quantitative Systems Pharmacology and In Silico Models in Drug Development: Current Needs, Gaps, and Challenges

2020 ◽  
Vol 9 (4) ◽  
pp. 195-197
Author(s):  
Flora T. Musuamba ◽  
Roberta Bursi ◽  
Efthymios Manolis ◽  
Kristin Karlsson ◽  
Alexander Kulesza ◽  
...  
Keyword(s):  
Drug Development ◽  
In Silico ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
In Silico Models

scholarly journals Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

2021 ◽  
Author(s):  
Limei Cheng ◽  
Yuchi Qiu ◽  
Brian J. Schmidt ◽  
Guo-Wei Wei
Keyword(s):  
Heart Failure ◽  
Research And Development ◽  
In Silico ◽  
Model Development ◽  
Pharmaceutical Research ◽  
Current Status ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Clinical Drug Development ◽  
Pharmaceutical Research And Development

AbstractQuantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.

scholarly journals From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

2020 ◽  
Author(s):  
Filippo Castiglione ◽  
Debashrito Deb ◽  
Anurag P. Srivastava ◽  
Pietro Liò ◽  
Arcangelo Liso
Keyword(s):  
Immune Response ◽  
Viral Load ◽  
Computational Modeling ◽  
In Silico ◽  
Vaccine Development ◽  
Real Life ◽  
Humoral Response ◽  
Immune Competence ◽  
Modeling Framework ◽  
The Impact

AbstractBackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important light to discuss hypothesis and design new experiments.

scholarly journals Ozonetherapy: a multirole weapon, topical pathway against SARS-COV-2

2020 ◽  
Author(s):  
Giovanni Tommaso Ranaldi ◽  
Emanuele Rocco Villani ◽  
Laura Franza ◽  
Giulia Motola
Keyword(s):  
Immune Response ◽  
Intensive Care ◽  
Viral Load ◽  
Microvascular Complications ◽  
Clinical Manifestations ◽  
Innate Immune ◽  
Cytokine Storm ◽  
Risk Of Mortality ◽  
Wide Range ◽  
Life Threatening

COVID-19 is the respiratory disease caused by the new coronavirus SARS-CoV-2 and is characterized by clinical manifestations ranging from mild, flu-like symptoms to severe respiratory and multi-organ failure. Patients with more severe symptoms may require intensive care treatments and face a high risk of mortality. COVID 19 is characterized by an abnormal inflammatory response similar to a cytokine storm, which is associated with endothelial dysfunction and microvascular complications. To date, no specific treatments are available for COVID-19 and its potentially life-threatening complications.Ozonetherapy is the administration of a mixture of ozone and oxygen (MO), which produces a series of benefits capable of counteracting a wide range of pathologies, in use for over a century as an unconventional medicine practice.Ozonetherapy using the techniques of small self-emo-infusion, and the topical application of ozonated oils or irrigation with ozonated water at the nasal level, could help to enhance the innate immune response at the level of the entrance ports in order to decrease the viral load and slow viral growth, especially in the early stages. In fact, recent studies show that nasal transport is likely to be a key feature of transmission, and drugs / vaccines administered intranasally could be highly effective in limiting spread.

scholarly journals From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling

2021 ◽  
Vol 12 ◽  
Author(s):  
Filippo Castiglione ◽  
Debashrito Deb ◽  
Anurag P. Srivastava ◽  
Pietro Liò ◽  
Arcangelo Liso
Keyword(s):  
Immune Response ◽  
Viral Load ◽  
In Silico ◽  
Vaccine Development ◽  
Real Life ◽  
Humoral Response ◽  
Population Level ◽  
Immune Competence ◽  
Modeling Framework ◽  
The Impact

BackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimStudies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.ResultsBy assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Comparative Study on Mitogen Activated Protein Kinase of Plasmodium Species by Using in silico Method

2012 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Mohd Fakharul Zaman Raja Yahya ◽  
Hasidah Mohd Sidek
Keyword(s):  
Amino Acid ◽  
In Silico ◽  
Nuclear Export ◽  
Nuclear Export Signal ◽  
Plasmodium Species ◽  
Mitogen Activated Protein Kinase ◽  
Multiple Sequence ◽  
Wide Range ◽  
Mitogen Activated Protein ◽  
Human Plasmodium

Malaria parasites, Plasmodium can infect a wide range of hosts including humans and rodents. There are two copies of mitogen activated protein kinases (MAPKs) in Plasmodium, namely MAPK1 and MAPK2. The MAPKs have been studied extensively in the human Plasmodium, P. falciparum. However, the MAPKs from other Plasmodium species have not been characterized and it is therefore the premise of presented study to characterize the MAPKs from other Plasmodium species-P. vivax, P. knowlesi, P. berghei, P. chabaudi and P.yoelli using a series of publicly available bioinformatic tools. In silico data indicates that all Plasmodium MAPKs are nuclear-localized and contain both a nuclear localization signal (NLS) and a Leucine-rich nuclear export signal (NES). The activation motifs of TDY and TSH were found to be fully conserved in Plasmodium MAPK1 and MAPK2, respectively. The detailed manual inspection of a multiple sequence alignment (MSA) construct revealed a total of 17 amino acid stack patterns comprising of different amino acids present in MAPKJ and MAPK2 respectively, with respect to rodent and human Plasmodia. It is proposed that these amino acid stack patterns may be useful in explaining the disparity between rodent and human Plasmodium MAPKs. 

Comparative Study on Mitogen Activated Protein Kinase of Plasmodium Species by Using In silico Method

2012 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Mohd Fakharul Zaman Raja Yahya ◽  
Hasidah Mohd Sidek
Keyword(s):  
Amino Acid ◽  
In Silico ◽  
Nuclear Export ◽  
Nuclear Export Signal ◽  
Plasmodium Species ◽  
Mitogen Activated Protein Kinase ◽  
Multiple Sequence ◽  
Bioinformatic Tools ◽  
Wide Range ◽  
Human Plasmodium

Malaria parasites, Plasmodium can infect a wide range ofhosts including humans and rodents. There are two copies ofmitogen activated protein kinases (MAPKs) in Plasmodium, namely MAPK1 and MAPK2. The MAPKs have been studied extensively in the human Plasmodium, P. falciparum. However, the MAPKs from other Plasmodium species have not been characterized and it is therefore the premise ofpresented study to characterize the MAPKs from other Plasmodium species-P. vivax, P. knowlesi, P. berghei, P. chabaudi and P.yoelli using a series ofpublicly available bioinformatic tools. In silico data indicates that all Plasmodium MAPKs are nuclear-localizedandcontain both a nuclear localization signal (NLS) anda Leucine-rich nuclear export signal (NES). The activation motifs ofTDYand TSH werefound to befully conserved in Plasmodium MAPK1 and MAPK2, respectively. The detailed manual inspection ofa multiple sequence alignment (MSA) construct revealed a total of 17 amino acid stack patterns comprising ofdifferent amino acids present in MAPK1 and MAPK2 respectively, with respect to rodent and human Plasmodia. 1t is proposed that these amino acid stack patterns may be useful in explaining the disparity between rodent and human Plasmodium MAPKs.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

2019 ◽  
Vol 18 (26) ◽  
pp. 2209-2229 ◽  
Author(s):  
Hai Pham-The ◽  
Miguel Á. Cabrera-Pérez ◽  
Nguyen-Hai Nam ◽  
Juan A. Castillo-Garit ◽  
Bakhtiyor Rasulev ◽  
...  
Keyword(s):  
Intestinal Absorption ◽  
In Silico ◽  
Review Paper ◽  
Cell Monolayer ◽  
Cell Permeability ◽  
Drug Substances ◽  
Wide Range ◽  
Modeling Techniques

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Chalcones: As potent α-amylase enzyme inhibitors; synthesis, in vitro, and in silico studies

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahboob Ali ◽  
Momin Khan ◽  
Khair Zaman ◽  
Abdul Wadood ◽  
Maryam Iqbal ◽  
...  
Keyword(s):  
In Silico ◽  
Enzyme Inhibitors ◽  
Biological Activities ◽  
Condensation Reaction ◽  
Type Ii Diabetes Mellitus ◽  
Spectroscopic Techniques ◽  
Chalcone Derivatives ◽  
In Silico Studies ◽  
Wide Range

: Background: The inhibition of α-amylase enzyme is one of the best therapeutic approach for the management of type II diabetes mellitus. Chalcone possesses a wide range of biological activities. Objective: In the current study chalcone derivatives (1-17) were synthesized and evaluated their inhibitory potential against α-amylase enzyme. Method: For that purpose, a library of substituted (E)-1-(naphthalene-2-yl)-3-phenylprop-2-en-1-ones was synthesized by ClaisenSchmidt condensation reaction of 2-acetonaphthanone and substituted aryl benzaldehyde in the presence of base and characterized via different spectroscopic techniques such as EI-MS, HREI-MS, 1H-, and 13C-NMR. Results: Sixteen synthetic chalcones were evaluated for in vitro porcine pancreatic α-amylase inhibition. All the chalcones demonstrated good inhibitory activities in the range of IC50 = 1.25 ± 1.05 to 2.40 ± 0.09 μM as compared to the standard commercial drug acarbose (IC50 = 1.34 ± 0.3 μM). Conclusion: Chalcone derivatives (1-17) were synthesized, characterized, and evaluated for their α-amylase inhibition. SAR revealed that electron donating groups in the phenyl ring have more influence on enzyme inhibition. However, to insight the participation of different substituents in the chalcones on the binding interactions with the α-amylase enzyme, in silico (computer simulation) molecular modeling analyses were carried out.

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