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Miura K. How to Accelerate Early Stage of Malaria Vaccine Development by Optimizing Functional Assays. Vaccines (Basel) 2024; 12:586. [PMID: 38932315 PMCID: PMC11209467 DOI: 10.3390/vaccines12060586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
While two Plasmodium falciparum circumsporozoite protein-based pre-erythrocytic vaccines (PEV), RTS,S and R21, have been approved by the WHO, no blood-stage vaccine (BSV) or transmission-blocking vaccine (TBV) has reached a phase 3 trial. One of the major obstacles that slows down malaria vaccine development is the shortage (or lack) of in vitro assays or animal models by which investigators can reasonably select the best vaccine formulation (e.g., antigen, adjuvant, or platform) and/or immunization strategy (e.g., interval of inoculation or route of immunization) before a human phase 2 trial. In the case of PEV, RTS,S and R21 have set a benchmark, and a new vaccine can be compared with (one of) the approved PEV directly in preclinical or early clinical studies. However, such an approach cannot be utilized for BSV or TBV development at this moment. The focus of this review is in vitro assays or in vivo models that can be used for P. falciparum BSV or TBV development, and I discuss important considerations during assay selection, standardization, qualification, validation, and interpretation of the assay results. Establishment of a robust assay/model with proper interpretation of the results is the one of key elements to accelerate future vaccine development.
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Affiliation(s)
- Kazutoyo Miura
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
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2
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Mørk SK, Skadborg SK, Albieri B, Draghi A, Bol K, Kadivar M, Westergaard MCW, Stoltenborg Granhøj J, Borch A, Petersen NV, Thuesen N, Rasmussen IS, Andreasen LV, Dohn RB, Yde CW, Noergaard N, Lorentzen T, Soerensen AB, Kleine-Kohlbrecher D, Jespersen A, Christensen D, Kringelum J, Donia M, Hadrup SR, Marie Svane I. Dose escalation study of a personalized peptide-based neoantigen vaccine (EVX-01) in patients with metastatic melanoma. J Immunother Cancer 2024; 12:e008817. [PMID: 38782542 PMCID: PMC11116868 DOI: 10.1136/jitc-2024-008817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Neoantigens can serve as targets for T cell-mediated antitumor immunity via personalized neopeptide vaccines. Interim data from our clinical study NCT03715985 showed that the personalized peptide-based neoantigen vaccine EVX-01, formulated in the liposomal adjuvant, CAF09b, was safe and able to elicit EVX-01-specific T cell responses in patients with metastatic melanoma. Here, we present results from the dose-escalation part of the study, evaluating the feasibility, safety, efficacy, and immunogenicity of EVX-01 in addition to anti-PD-1 therapy. METHODS Patients with metastatic melanoma on anti-PD-1 therapy were treated in three cohorts with increasing vaccine dosages (twofold and fourfold). Tumor-derived neoantigens were selected by the AI platform PIONEER and used in personalized therapeutic cancer peptide vaccines EVX-01. Vaccines were administered at 2-week intervals for a total of three intraperitoneal and three intramuscular injections. The study's primary endpoint was safety and tolerability. Additional endpoints were immunological responses, survival, and objective response rates. RESULTS Compared with the base dose level previously reported, no new vaccine-related serious adverse events were observed during dose escalation of EVX-01 in combination with an anti-PD-1 agent given according to local guidelines. Two patients at the third dose level (fourfold dose) developed grade 3 toxicity, most likely related to pembrolizumab. Overall, 8 out of the 12 patients had objective clinical responses (6 partial response (PR) and 2 CR), with all 4 patients at the highest dose level having a CR (1 CR, 3 PR). EVX-01 induced peptide-specific CD4+ and/or CD8+T cell responses in all treated patients, with CD4+T cells as the dominating responses. The magnitude of immune responses measured by IFN-γ ELISpot assay correlated with individual peptide doses. A significant correlation between the PIONEER quality score and induced T cell immunogenicity was detected, while better CRs correlated with both the number of immunogenic EVX-01 peptides and the PIONEER quality score. CONCLUSION Immunization with EVX-01-CAF09b in addition to anti-PD-1 therapy was shown to be safe and well tolerated and elicit vaccine neoantigen-specific CD4+and CD8+ T cell responses at all dose levels. In addition, objective tumor responses were observed in 67% of patients. The results encourage further assessment of the antitumor efficacy of EVX-01 in combination with anti-PD-1 therapy.
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Affiliation(s)
- Sofie Kirial Mørk
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
| | | | - Benedetta Albieri
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
| | - Arianna Draghi
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
| | - Kalijn Bol
- Medical Oncology, Radboudumc, Nijmegen, The Netherlands
| | - Mohammad Kadivar
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Joachim Stoltenborg Granhøj
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
| | - Annie Borch
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | | | | | | | - Rebecca Bach Dohn
- Center for Vaccine Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Nis Noergaard
- Department of Urology, Copenhagen University Hospital, Herlev, Denmark
| | - Torben Lorentzen
- Department of Gastroenterology, Copenhagen University Hospital, Herlev, Denmark
| | | | | | | | - Dennis Christensen
- Center for Vaccine Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Marco Donia
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Inge Marie Svane
- Department of Oncology, Copenhagen University Hospital, National Center for Cancer Immune Therapy (CCIT-DK), Herlev, Denmark
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Hartmeier PR, Ostrowski SM, Busch EE, Empey KM, Meng WS. Lymphatic distribution considerations for subunit vaccine design and development. Vaccine 2024; 42:2519-2529. [PMID: 38494411 DOI: 10.1016/j.vaccine.2024.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/30/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024]
Abstract
Subunit vaccines are an important platform for controlling current and emerging infectious diseases. The lymph nodes are the primary site generating the humoral response and delivery of antigens to these sites is critical to effective immunization. Indeed, the duration of antigen exposure within the lymph node is correlated with the antibody response. While current licensed vaccines are typically given through the intramuscular route, injecting vaccines subcutaneously allows for direct access to lymphatic vessels and therefore can enhance the transfer of antigen to the lymph nodes. However, protein subunit antigen uptake into the lymph nodes is inefficient, and subunit vaccines require adjuvants to stimulate the initial immune response. Therefore, formulation strategies have been developed to enhance the exposure of subunit proteins and adjuvants to the lymph nodes by increasing lymphatic uptake or prolonging the retention at the injection site. Given that lymph node exposure is a crucial consideration in vaccine design, in depth analyses of the pharmacokinetics of antigens and adjuvants should be the focus of future preclinical and clinical studies. This review will provide an overview of formulation strategies for targeting the lymphatics and prolonging antigen exposure and will discuss pharmacokinetic evaluations which can be applied toward vaccine development.
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Affiliation(s)
- Paul R Hartmeier
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA
| | - Sarah M Ostrowski
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, PA 15213, USA
| | - Emelia E Busch
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA
| | - Kerry M Empey
- Center for Clinical Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA 15213, USA; Department of Immunology, School of Medicine University of Pittsburgh, PA 15213, USA
| | - Wilson S Meng
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PA 15219, USA.
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4
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Ibrahim EIK, Ellingsen EB, Mangsbo SM, Friberg LE. Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model. Int Immunopharmacol 2024; 126:111225. [PMID: 37988911 DOI: 10.1016/j.intimp.2023.111225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/10/2023] [Accepted: 11/10/2023] [Indexed: 11/23/2023]
Abstract
Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.
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Affiliation(s)
| | - Espen B Ellingsen
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; Ultimovacs ASA, Oslo, Norway
| | - Sara M Mangsbo
- Department of Pharmacy, Uppsala University, Uppsala, Sweden; Ultimovacs AB, Uppsala, Sweden
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.
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Desikan R, Germani M, van der Graaf PH, Magee M. A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development. J Pharm Sci 2024; 113:22-32. [PMID: 37924975 DOI: 10.1016/j.xphs.2023.10.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/27/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023]
Abstract
Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modelling & Simulation, GSK, United Kingdom.
| | | | - Piet H van der Graaf
- Certara QSP, Canterbury Innovation Centre, University Road, Canterbury CT2 7FG, United Kingdom; Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333 CC Leiden, Netherlands
| | - Mindy Magee
- Clinical Pharmacology Modelling & Simulation, GSK, United States
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Pizzitutti F, Bonnet G, Gonzales-Gustavson E, Gabriël S, Pan WK, Gonzalez AE, Garcia HH, O'Neal SE. Spatial transferability of an agent-based model to simulate Taenia solium control interventions. Parasit Vectors 2023; 16:410. [PMID: 37941062 PMCID: PMC10634186 DOI: 10.1186/s13071-023-06003-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Models can be used to study and predict the impact of interventions aimed at controlling the spread of infectious agents, such as Taenia solium, a zoonotic parasite whose larval stage causes epilepsy and economic loss in many rural areas of the developing nations. To enhance the credibility of model estimates, calibration against observed data is necessary. However, this process may lead to a paradoxical dependence of model parameters on location-specific data, thus limiting the model's geographic transferability. METHODS In this study, we adopted a non-local model calibration approach to assess whether it can improve the spatial transferability of CystiAgent, our agent-based model of local-scale T. solium transmission. The calibration dataset for CystiAgent consisted of cross-sectional data on human taeniasis, pig cysticercosis and pig serology collected in eight villages in Northwest Peru. After calibration, the model was transferred to a second group of 21 destination villages in the same area without recalibrating its parameters. Model outputs were compared to pig serology data collected over a period of 2 years in the destination villages during a trial of T. solium control interventions, based on mass and spatially targeted human and pig treatments. RESULTS Considering the uncertainties associated with empirical data, the model produced simulated pre-intervention pig seroprevalences that were successfully validated against data collected in 81% of destination villages. Furthermore, the model outputs were able to reproduce validated pig seroincidence values in 76% of destination villages when compared to the data obtained after the interventions. The results demonstrate that the CystiAgent model, when calibrated using a non-local approach, can be successfully transferred without requiring additional calibration. CONCLUSIONS This feature allows the model to simulate both baseline pre-intervention transmission conditions and the outcomes of control interventions across villages that form geographically homogeneous regions, providing a basis for developing large-scale models representing T. solium transmission at a regional level.
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Affiliation(s)
| | - Gabrielle Bonnet
- Centre for Mathematical Modelling of Infectious Disease (CMMID), Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Eloy Gonzales-Gustavson
- Tropical and Highlands Veterinary Research Institute, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Sarah Gabriël
- Department of Veterinary Public Health and Food Safety, Ghent University, Ghent, Belgium
| | - William K Pan
- Nicholas School of Environment and Duke Global Health Institute, Duke University, Durham, USA
| | - Armando E Gonzalez
- School of Veterinary Medicine, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Hector H Garcia
- Center for Global Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- Cysticercosis Unit, National Institute of Neurological Sciences, Lima, Peru
| | - Seth E O'Neal
- Center for Global Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- School of Public Health, Oregon Health & Science University and Portland State University, Portland, USA
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Jha SK, Imran M, Jha LA, Hasan N, Panthi VK, Paudel KR, Almalki WH, Mohammed Y, Kesharwani P. A Comprehensive review on Pharmacokinetic Studies of Vaccines: Impact of delivery route, carrier-and its modulation on immune response. ENVIRONMENTAL RESEARCH 2023; 236:116823. [PMID: 37543130 DOI: 10.1016/j.envres.2023.116823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023]
Abstract
The lack of knowledge about the absorption, distribution, metabolism, and excretion (ADME) of vaccines makes former biopharmaceutical optimization difficult. This was shown during the COVID-19 immunization campaign, where gradual booster doses were introduced.. Thus, understanding vaccine ADME and its effects on immunization effectiveness could result in a more logical vaccine design in terms of formulation, method of administration, and dosing regimens. Herein, we will cover the information available on vaccine pharmacokinetics, impacts of delivery routes and carriers on ADME, utilization and efficiency of nanoparticulate delivery vehicles, impact of dose level and dosing schedule on the therapeutic efficacy of vaccines, intracellular and endosomal trafficking and in vivo fate, perspective on DNA and mRNA vaccines, new generation sequencing and mathematical models to improve cancer vaccination and pharmacology, and the reported toxicological study of COVID-19 vaccines. Altogether, this review will enhance the reader's understanding of the pharmacokinetics of vaccines and methods that can be implied in delivery vehicle design to improve the absorption and distribution of immunizing agents and estimate the appropriate dose to achieve better immunogenic responses and prevent toxicities.
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Affiliation(s)
- Saurav Kumar Jha
- Department of Biomedicine, Health & Life Convergence Sciences, Mokpo National University, Muan-gun, Jeonnam, 58554, Republic of Korea; Department of Biological Sciences and Bioengineering (BSBE), Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India.
| | - Mohammad Imran
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, 4102, Australia
| | - Laxmi Akhileshwar Jha
- H. K. College of Pharmacy, Mumbai University, Pratiksha Nagar, Jogeshwari, West Mumbai, 400102, India
| | - Nazeer Hasan
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India
| | - Vijay Kumar Panthi
- Department of Pharmacy, College of Pharmacy and Natural Medicine Research Institute, Mokpo National University, Jeonnam, 58554, Republic of Korea
| | - Keshav Raj Paudel
- Centre for Inflammation, Faculty of Science, School of Life Science, Centenary Institute and University of Technology Sydney, Sydney, 2007, Australia
| | - Waleed H Almalki
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Umm Al-Qura University, Makkah, 24381, Saudi Arabia
| | - Yousuf Mohammed
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, 4102, Australia
| | - Prashant Kesharwani
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India; Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
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Kerr MD, Johnson WT, McBride DA, Chumber AK, Shah NJ. Biodegradable scaffolds for enhancing vaccine delivery. Bioeng Transl Med 2023; 8:e10591. [PMID: 38023723 PMCID: PMC10658593 DOI: 10.1002/btm2.10591] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 12/01/2023] Open
Abstract
Sustained release of vaccine components is a potential method to boost efficacy compared with traditional bolus injection. Here, we show that a biodegradable hyaluronic acid (HA)-scaffold, termed HA cryogel, mediates sustained antigen and adjuvant release in vivo leading to a durable immune response. Delivery from subcutaneously injected HA cryogels was assessed and a formulation which enhanced the immune response while minimizing the inflammation associated with the foreign body response was identified, termed CpG-OVA-HAC2. Dose escalation studies with CpG-OVA-HAC2 demonstrated that both the antibody and T cell responses were dose-dependent and influenced by the competency of neutrophils to perform oxidative burst. In immunodeficient post-hematopoietic stem cell transplanted mice, immunization with CpG-OVA-HAC2 elicited a strong antibody response, three orders of magnitude higher than dose-matched bolus injection. In a melanoma model, CpG-OVA-HAC2 induced dose-responsive prophylactic protection, slowing the tumor growth rate and enhancing overall survival. Upon rechallenge, none of the mice developed new tumors suggesting the development of robust immunological memory and long-lasting protection against repeat infections. CpG-OVA-HAC2 also enhanced survival in mice with established tumors. The results from this work support the potential for CpG-OVA-HAC2 to enhance vaccine delivery.
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Affiliation(s)
- Matthew D. Kerr
- Department of NanoengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
- Chemical Engineering ProgramUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Wade T. Johnson
- Department of NanoengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
| | - David A. McBride
- Department of NanoengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
- Chemical Engineering ProgramUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Arun K. Chumber
- Department of NanoengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
- Chemical Engineering ProgramUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Nisarg J. Shah
- Department of NanoengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
- Chemical Engineering ProgramUniversity of California San DiegoLa JollaCaliforniaUSA
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Leon C, Tokarev A, Bouchnita A, Volpert V. Modelling of the Innate and Adaptive Immune Response to SARS Viral Infection, Cytokine Storm and Vaccination. Vaccines (Basel) 2023; 11:vaccines11010127. [PMID: 36679972 PMCID: PMC9861811 DOI: 10.3390/vaccines11010127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/20/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023] Open
Abstract
In this work, we develop mathematical models of the immune response to respiratory viral infection, taking into account some particular properties of the SARS-CoV infections, cytokine storm and vaccination. Each model consists of a system of ordinary differential equations that describe the interactions of the virus, epithelial cells, immune cells, cytokines, and antibodies. Conventional analysis of the existence and stability of stationary points is completed by numerical simulations in order to study the dynamics of solutions. The behavior of the solutions is characterized by large peaks of virus concentration specific to acute respiratory viral infections. At the first stage, we study the innate immune response based on the protective properties of interferon secreted by virus-infected cells. Viral infection down-regulates interferon production. This competition can lead to the bistability of the system with different regimes of infection progression with high or low intensity. After that, we introduce the adaptive immune response with antigen-specific T- and B-lymphocytes. The resulting model shows how the incubation period and the maximal viral load depend on the initial viral load and the parameters of the immune response. In particular, an increase in the initial viral load leads to a shorter incubation period and higher maximal viral load. The model shows that a deficient production of antibodies leads to an increase in the incubation period and even higher maximum viral loads. In order to study the emergence and dynamics of cytokine storm, we consider proinflammatory cytokines produced by cells of the innate immune response. Depending on the parameters of the model, the system can remain in the normal inflammatory state specific for viral infections or, due to positive feedback between inflammation and immune cells, pass to cytokine storm characterized by the excessive production of proinflammatory cytokines. Finally, we study the production of antibodies due to vaccination. We determine the dose-response dependence and the optimal interval of vaccine dose. Assumptions of the model and obtained results correspond to the experimental and clinical data.
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Affiliation(s)
- Cristina Leon
- Interdisciplinary Center for Mathematical Modelling in Biomedicine, S.M. Nikol’skii Mathematical Institute, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
- M&S Decisions, 5 Naryshkinskaya Alley, 125167 Moscow, Russia
- Department of Foreign Languages No. 2, Plekhanov Russian University of Economics, 36 Stremyanny Lane, 115093 Moscow, Russia
- Correspondence:
| | - Alexey Tokarev
- Interdisciplinary Center for Mathematical Modelling in Biomedicine, S.M. Nikol’skii Mathematical Institute, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
- Semenov Institute of Chemical Physics, 4 Kosygin St., 119991 Moscow, Russia
- Bukhara Engineering Technological Institute, 15 Murtazoyeva Street, Bukhara 200100, Uzbekistan
| | - Anass Bouchnita
- Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79902, USA
| | - Vitaly Volpert
- Interdisciplinary Center for Mathematical Modelling in Biomedicine, S.M. Nikol’skii Mathematical Institute, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622 Villeurbanne, France
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Rhodes S, Smith N, Evans T, White R. Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling. Vaccine 2022; 40:7032-7041. [PMID: 36272876 PMCID: PMC9574467 DOI: 10.1016/j.vaccine.2022.10.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Identifying optimal COVID-19 vaccine dose is essential for maximizing their impact. However, COVID-19 vaccine dose-finding has been an empirical process, limited by short development timeframes, and therefore potentially not thoroughly investigated. Mathematical IS/ID modelling is a novel method for predicting optimal vaccine dose which could inform future COVID-19 vaccine dose decision making. METHODS Published clinical data on COVID-19 vaccine dose-response was identified and extracted. Mathematical models were calibrated to the dose-response data stratified by subpopulation, where possible to predict optimal dose. Predicted optimal doses were summarised across vaccine type and compared to chosen dose for the primary series of COVID-19 vaccines to identify vaccine doses that may benefit from re-evaluation. RESULTS 30 clinical dose-response datasets in adults and elderly population were extracted for four vaccine types and optimal doses predicted using the models. Results suggest that, if re-assessed for dose, COVID-19 vaccines Ad26.cov, ChadOx1 n-Cov19, BNT162b2, Coronavac, and NVX-CoV2373 could benefit from increased dose in adults and mRNA-1273 and Coronavac, could benefit from increased and decreased dose for the elderly population, respectively. DISCUSSION Future iterations of COVID-19 vaccines could benefit from re-evaluating dose to ensure most effective use of the vaccine and mathematical modelling can support this.
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Affiliation(s)
- Sophie Rhodes
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK,Corresponding author
| | - Neal Smith
- Defence and Science Technology Laboratory, UK
| | | | - Richard White
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK
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Rudi E, Martin Aispuro P, Zurita E, Gonzalez Lopez Ledesma M, Bottero D, Malito J, Gabrielli M, Gaillard E, Stuible M, Durocher Y, Gamarnik A, Wigdorovitz A, Hozbor D. Immunological study of COVID-19 vaccine candidate based on recombinant spike trimer protein from different SARS-CoV-2 variants of concern. Front Immunol 2022; 13:1020159. [PMID: 36248791 PMCID: PMC9560800 DOI: 10.3389/fimmu.2022.1020159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
The emergency of new SARS-CoV-2 variants that feature increased immune escape marks an urgent demand for better vaccines that will provide broader immunogenicity. Here, we evaluated the immunogenic capacity of vaccine candidates based on the recombinant trimeric spike protein (S) of different SARS-CoV-2 variants of concern (VOC), including the ancestral Wuhan, Beta and Delta viruses. In particular, we assessed formulations containing either single or combined S protein variants. Our study shows that the formulation containing the single S protein from the ancestral Wuhan virus at a concentration of 2µg (SW2-Vac 2µg) displayed in the mouse model the highest IgG antibody levels against all the three (Wuhan, Beta, and Delta) SARS-CoV-2 S protein variants tested. In addition, this formulation induced significantly higher neutralizing antibody titers against the three viral variants when compared with authorized Gam-COVID-Vac-rAd26/rAd5 (Sputnik V) or ChAdOx1 (AstraZeneca) vaccines. SW2-Vac 2µg was also able to induce IFN-gamma and IL-17, memory CD4 populations and follicular T cells. Used as a booster dose for schedules performed with different authorized vaccines, SW2-Vac 2µg vaccine candidate also induced higher levels of total IgG and IgG isotypes against S protein from different SARS-CoV-2 variants in comparison with those observed with homologous 3-dose schedule of Sputnik V or AstraZeneca. Moreover, SW2-Vac 2µg booster induced broadly strong neutralizing antibody levels against the three tested SARS-CoV-2 variants. SW2-Vac 2µg booster also induced CD4+ central memory, CD4+ effector and CD8+ populations. Overall, the results demonstrate that SW2-Vac 2 µg is a promising formulation for the development of a next generation COVID-19 vaccine.
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Affiliation(s)
- Erika Rudi
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | - Pablo Martin Aispuro
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | - Eugenia Zurita
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | | | - Daniela Bottero
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | - Juan Malito
- INCUINTA INTA, CONICET, HURLINGHAM, INTA Castelar, Buenos Aires, Argentina
| | - Magali Gabrielli
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | - Emilia Gaillard
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
| | - Matthew Stuible
- Human Health Therapeutics Research Center, National Research Council Canada, Montreal, QC, Canada
| | - Yves Durocher
- Human Health Therapeutics Research Center, National Research Council Canada, Montreal, QC, Canada
| | | | - Andrés Wigdorovitz
- INCUINTA INTA, CONICET, HURLINGHAM, INTA Castelar, Buenos Aires, Argentina
| | - Daniela Hozbor
- Laboratorio VacSal, Instituto de Biotecnología y Biología Molecular (IBBM), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Centro Científico Tecnológico – Consejo Nacional de Investigaciones Científicas y Técnicas (CCT-CONICET), La Plata, Argentina
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12
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Benest J, Rhodes S, Evans TG, White RG. Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity. Vaccines (Basel) 2022; 10:vaccines10050756. [PMID: 35632511 PMCID: PMC9144167 DOI: 10.3390/vaccines10050756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/03/2022] [Accepted: 05/06/2022] [Indexed: 02/06/2023] Open
Abstract
Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose–efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
- Correspondence:
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
| | - Thomas G. Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK;
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.G.W.)
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14
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Long-Term In Vitro Passaging Had a Negligible Effect on Extracellular Vesicles Released by Leishmania amazonensis and Induced Protective Immune Response in BALB/c Mice. J Immunol Res 2022; 2021:7809637. [PMID: 34977257 PMCID: PMC8720021 DOI: 10.1155/2021/7809637] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/07/2021] [Accepted: 11/25/2021] [Indexed: 12/03/2022] Open
Abstract
Depending on Leishmania species and the presence/absence of virulence factors, Leishmania extracellular vesicles (EVs) can differently stimulate host immune cells. This work is aimed at characterizing and evaluating the protective role of EVs released by Leishmania amazonensis promastigotes under different maintenance conditions. Initially, using a control strain, we standardized 26°C as the best release temperature to obtain EVs with a potential protective role in the experimental leishmaniasis model. Then, long-term (LT-P) promastigotes of L. amazonensis were obtained after long-term in vitro culture (100 in vitro passages). In vivo-derived (IVD-P) promastigotes of L. amazonensis were selected after 3 consecutive experimental infections in BALB/c mice. Those strains developed similar lesion sizes except for IVD-P at 8 weeks post infection. No differences in EV production were detected in both strains. However, the presence of LPG between LT-P and IVD-P EVs was different. Groups of mice immunized with EVs emulsified in the adjuvant and challenged with IVD-P parasites showed decreased lesion size and parasitic load compared with the nonimmunized groups. The immunization regimen with two doses showed high IFN-γ and IgG2a titers in challenged mice with either IVD-P or LT-P EVs. IL-4 and IL-10 were detected in immunized mice, suggesting a mixed Th1/Th2 profile. EVs released by either IVD-P or LT-P induced a partial protective effect in an immunization model. Thus, our results uncover a potential protective role of EVs from L. amazonensis for cutaneous leishmaniasis. Moreover, long-term maintenance under in vitro conditions did not seem to affect EV release and their immunization properties in mice.
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15
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Garg AK, Mittal S, Padmanabhan P, Desikan R, Dixit NM. Increased B Cell Selection Stringency In Germinal Centers Can Explain Improved COVID-19 Vaccine Efficacies With Low Dose Prime or Delayed Boost. Front Immunol 2021; 12:776933. [PMID: 34917089 PMCID: PMC8669483 DOI: 10.3389/fimmu.2021.776933] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/10/2021] [Indexed: 12/17/2022] Open
Abstract
The efficacy of COVID-19 vaccines appears to depend in complex ways on the vaccine dosage and the interval between the prime and boost doses. Unexpectedly, lower dose prime and longer prime-boost intervals have yielded higher efficacies in clinical trials. To elucidate the origins of these effects, we developed a stochastic simulation model of the germinal center (GC) reaction and predicted the antibody responses elicited by different vaccination protocols. The simulations predicted that a lower dose prime could increase the selection stringency in GCs due to reduced antigen availability, resulting in the selection of GC B cells with higher affinities for the target antigen. The boost could relax this selection stringency and allow the expansion of the higher affinity GC B cells selected, improving the overall response. With a longer dosing interval, the decay in the antigen with time following the prime could further increase the selection stringency, amplifying this effect. The effect remained in our simulations even when new GCs following the boost had to be seeded by memory B cells formed following the prime. These predictions offer a plausible explanation of the observed paradoxical effects of dosage and dosing interval on vaccine efficacy. Tuning the selection stringency in the GCs using prime-boost dosages and dosing intervals as handles may help improve vaccine efficacies.
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Affiliation(s)
- Amar K. Garg
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Soumya Mittal
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Pranesh Padmanabhan
- Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Rajat Desikan
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
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16
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Maas BM, Lommerse J, Plock N, Railkar RA, Cheung SYA, Caro L, Chen J, Liu W, Zhang Y, Huang Q, Gao W, Qin L, Meng J, Witjes H, Schindler E, Guiastrennec B, Bellanti F, Spellman DS, Roadcap B, Kalinova M, Fok-Seang J, Catchpole AP, Espeseth AS, Stoch SA, Lai E, Vora KA, Aliprantis AO, Sachs JR. Forward and reverse translational approaches to predict efficacy of neutralizing respiratory syncytial virus (RSV) antibody prophylaxis. EBioMedicine 2021; 73:103651. [PMID: 34775220 PMCID: PMC8603022 DOI: 10.1016/j.ebiom.2021.103651] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/29/2021] [Accepted: 10/12/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Neutralizing mAbs can prevent communicable viral diseases. MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) under development to prevent RSV infection in infants. Development and validation of methods to predict efficacious doses of neutralizing antibodies across patient populations exposed to a time-varying force of infection (i.e., seasonal variation) are necessary. METHODS Five decades of clinical trial literature were leveraged to build a model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinical endpoints. The MBMA was validated by backward translation to animal challenge experiments and forward translation to predict results of a recent RSV mAb trial. MBMA predictions were evaluated against a human trial of 70 participants who received either placebo or one of four dose-levels of MK-1654 and were challenged with RSV [NCT04086472]. The MBMA was used to perform clinical trial simulations and predict efficacy of MK-1654 in the infant target population. FINDINGS The MBMA established a quantitative relationship between RSV SNA and clinical endpoints. This relationship was quantitatively consistent with animal model challenge experiments and results of a recently published clinical trial. Additionally, SNA elicited by increasing doses of MK-1654 in humans reduced RSV symptomatic infection rates with a quantitative relationship that approximated the MBMA. The MBMA indicated a high probability that a single dose of ≥ 75 mg of MK-1654 will result in prophylactic efficacy (> 75% for 5 months) in infants. INTERPRETATION An MBMA approach can predict efficacy of neutralizing antibodies against RSV and potentially other respiratory pathogens.
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Affiliation(s)
- Brian M Maas
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Jos Lommerse
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | - Nele Plock
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | - Radha A Railkar
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - S Y Amy Cheung
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | - Luzelena Caro
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Jingxian Chen
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Wen Liu
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Ying Zhang
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Qinlei Huang
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Wei Gao
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Li Qin
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | - Jie Meng
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | - Han Witjes
- Certara, 100 Overlook Center STE 101, Princeton, NJ 08540, USA
| | | | | | | | - Daniel S Spellman
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Brad Roadcap
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | | | | | | | - Amy S Espeseth
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - S Aubrey Stoch
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Eseng Lai
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | - Kalpit A Vora
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA
| | | | - Jeffrey R Sachs
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, NJ 07033, USA.
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17
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Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172:249-274. [PMID: 33561453 PMCID: PMC7871111 DOI: 10.1016/j.addr.2021.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
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Affiliation(s)
- Woochang Hwang
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Winnie Lei
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Nicholas M Katritsis
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Méabh MacMahon
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Centre for Therapeutics Discovery, LifeArc, Stevenage, UK
| | - Kathryn Chapman
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
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18
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Molari M, Monasson R, Cocco S. Survival probability and size of lineages in antibody affinity maturation. Phys Rev E 2021; 103:052413. [PMID: 34134280 DOI: 10.1103/physreve.103.052413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 05/06/2021] [Indexed: 11/07/2022]
Abstract
Affinity maturation (AM) is the process through which the immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines. At its core AM is analogous to a Darwinian evolutionary process, where B cells mutate and are selected on the base of their affinity for an antigen (Ag), and Ag availability tunes the selective pressure. In cases when this selective pressure is high, the number of B cells might quickly decrease and the population might risk extinction in what is known as a population bottleneck. Here we study the probability for a B-cell lineage to survive this bottleneck scenario as a function of the progenitor affinity for the Ag. Using recursive relations and probability generating functions we derive expressions for the average extinction time and progeny size for lineages that go extinct. We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size. Our study suggests the population bottleneck phenomenology might represent a limit case in the space of biologically plausible maturation scenarios, whose characterization could help guide the process of vaccine development.
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Affiliation(s)
- Marco Molari
- Laboratoire de Physique de l'École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université de Paris, 24 rue Lhomond, 75005 Paris, France
| | - Rémi Monasson
- Laboratoire de Physique de l'École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université de Paris, 24 rue Lhomond, 75005 Paris, France
| | - Simona Cocco
- Laboratoire de Physique de l'École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université de Paris, 24 rue Lhomond, 75005 Paris, France
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Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Multiscale modeling meets machine learning: What can we learn? ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:1017-1037. [PMID: 34093005 PMCID: PMC8172124 DOI: 10.1007/s11831-020-09405-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/09/2020] [Indexed: 05/10/2023]
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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Affiliation(s)
| | - Mark Alber
- University of California, Riverside, USA
| | | | - William R Cannon
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Suvranu De
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | | | | | | | | | | | - Linda Petzold
- University of California, Santa Barbara, California, USA
| | - Ellen Kuhl
- Stanford University, Stanford, California, USA
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20
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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21
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Benest J, Rhodes S, Quaife M, Evans TG, White RG. Optimising Vaccine Dose in Inoculation against SARS-CoV-2, a Multi-Factor Optimisation Modelling Study to Maximise Vaccine Safety and Efficacy. Vaccines (Basel) 2021; 9:vaccines9020078. [PMID: 33499326 PMCID: PMC7911627 DOI: 10.3390/vaccines9020078] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/17/2021] [Accepted: 01/20/2021] [Indexed: 01/05/2023] Open
Abstract
Developing a vaccine against the global pandemic SARS-CoV-2 is a critical area of active research. Modelling can be used to identify optimal vaccine dosing; maximising vaccine efficacy and safety and minimising cost. We calibrated statistical models to published dose-dependent seroconversion and adverse event data of a recombinant adenovirus type-5 (Ad5) SARS-CoV-2 vaccine given at doses 5.0 × 1010, 1.0 × 1011 and 1.5 × 1011 viral particles. We estimated the optimal dose for three objectives, finding: (A) the minimum dose that may induce herd immunity, (B) the dose that maximises immunogenicity and safety and (C) the dose that maximises immunogenicity and safety whilst minimising cost. Results suggest optimal dose [95% confidence interval] in viral particles per person was (A) 1.3 × 1011 [0.8–7.9 × 1011], (B) 1.5 × 1011 [0.3–5.0 × 1011] and (C) 1.1 × 1011 [0.2–1.5 × 1011]. Optimal dose exceeded 5.0 × 1010 viral particles only if the cost of delivery exceeded £0.65 or cost per 1011 viral particles was less than £6.23. Optimal dose may differ depending on the objectives of developers and policy-makers, but further research is required to improve the accuracy of optimal-dose estimates.
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (M.Q.); (R.G.W.)
- Correspondence:
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (M.Q.); (R.G.W.)
| | - Matthew Quaife
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (M.Q.); (R.G.W.)
| | - Thomas G. Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK;
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (M.Q.); (R.G.W.)
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Kamiya T, Greischar MA, Schneider DS, Mideo N. Uncovering drivers of dose-dependence and individual variation in malaria infection outcomes. PLoS Comput Biol 2020; 16:e1008211. [PMID: 33031367 PMCID: PMC7544130 DOI: 10.1371/journal.pcbi.1008211] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 07/31/2020] [Indexed: 01/01/2023] Open
Abstract
To understand why some hosts get sicker than others from the same type of infection, it is essential to explain how key processes, such as host responses to infection and parasite growth, are influenced by various biotic and abiotic factors. In many disease systems, the initial infection dose impacts host morbidity and mortality. To explore drivers of dose-dependence and individual variation in infection outcomes, we devised a mathematical model of malaria infection that allowed host and parasite traits to be linear functions (reaction norms) of the initial dose. We fitted the model, using a hierarchical Bayesian approach, to experimental time-series data of acute Plasmodium chabaudi infection across doses spanning seven orders of magnitude. We found evidence for both dose-dependent facilitation and debilitation of host responses. Most importantly, increasing dose reduced the strength of activation of indiscriminate host clearance of red blood cells while increasing the half-life of that response, leading to the maximal response at an intermediate dose. We also explored the causes of diverse infection outcomes across replicate mice receiving the same dose. Besides random noise in the injected dose, we found variation in peak parasite load was due to unobserved individual variation in host responses to clear infected cells. Individual variation in anaemia was likely driven by random variation in parasite burst size, which is linked to the rate of host cells lost to malaria infection. General host vigour in the absence of infection was also correlated with host health during malaria infection. Our work demonstrates that the reaction norm approach provides a useful quantitative framework for examining the impact of a continuous external factor on within-host infection processes.
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Affiliation(s)
- Tsukushi Kamiya
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada
| | - Megan A. Greischar
- Department of Ecology Evolutionary Biology, Cornell University, United States of America
| | - David S. Schneider
- Program in Immunology, Stanford University, Stanford, California, United States of America
- Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America
| | - Nicole Mideo
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada
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23
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Shemesh CS, Hsu JC, Hosseini I, Shen BQ, Rotte A, Twomey P, Girish S, Wu B. Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities. Mol Ther 2020; 29:555-570. [PMID: 33038322 DOI: 10.1016/j.ymthe.2020.09.038] [Citation(s) in RCA: 130] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/15/2020] [Accepted: 09/26/2020] [Indexed: 12/21/2022] Open
Abstract
Tremendous innovation is underway among a rapidly expanding repertoire of promising personalized immune-based treatments. Therapeutic cancer vaccines (TCVs) are attractive systemic immunotherapies that activate and expand antigen-specific CD8+ and CD4+ T cells to enhance anti-tumor immunity. Our review highlights key issues impacting TCVs in clinical practice and reports on progress in development. We review the mechanism of action, immune-monitoring, dosing strategies, combinations, obstacles, and regulation of cancer vaccines. Most trials of personalized TCVs are ongoing and represent diverse platforms with predominantly early investigations of mRNA, DNA, or peptide-based targeting strategies against neoantigens in solid tumors, with many in combination immunotherapies. Multiple delivery systems, routes of administration, and dosing strategies are used. Intravenous or intramuscular administration is common, including delivery by lipid nanoparticles. Absorption and biodistribution impact antigen uptake, expression, and presentation, affecting the strength, speed, and duration of immune response. The emerging trials illustrate the complexity of developing this class of innovative immunotherapies. Methodical testing of the multiple potential factors influencing immune responses, as well as refined quantitative methodologies to facilitate optimal dosing strategies, could help resolve uncertainty of therapeutic approaches. To increase the likelihood of success in bringing these medicines to patients, several unique development challenges must be overcome.
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Affiliation(s)
- Colby S Shemesh
- Department of Clinical Pharmacology Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
| | - Joy C Hsu
- Department of Clinical Pharmacology Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Iraj Hosseini
- Department of Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Ben-Quan Shen
- Department of Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Anand Rotte
- Department of Clinical Pharmacology Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Patrick Twomey
- Department of Product Development Safety, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Sandhya Girish
- Department of Clinical Pharmacology Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Benjamin Wu
- Department of Clinical Pharmacology Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
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Abstract
PURPOSE OF REVIEW Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential. RECENT FINDINGS Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement. SUMMARY This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.
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Molari M, Eyer K, Baudry J, Cocco S, Monasson R. Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers. eLife 2020; 9:e55678. [PMID: 32538783 PMCID: PMC7360369 DOI: 10.7554/elife.55678] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 06/12/2020] [Indexed: 12/11/2022] Open
Abstract
Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modeling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.
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Affiliation(s)
- Marco Molari
- Laboratoire de Physique de l’École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université Paris-Diderot, Sorbonne Paris CitéParisFrance
| | - Klaus Eyer
- Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, ETH ZurichZurichSwitzerland
| | - Jean Baudry
- Laboratoire Colloides et Materiaux Divises (LCMD), Chemistry, Biology and Innovation (CBI), ESPCI, PSL Research and CNRSParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université Paris-Diderot, Sorbonne Paris CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’École Normale Supérieure, ENS, PSL University, CNRS UMR8023, Sorbonne Université, Université Paris-Diderot, Sorbonne Paris CitéParisFrance
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26
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Benest J, Rhodes S, Afrough S, Evans T, White R. Response Type and Host Species may be Sufficient to Predict Dose-Response Curve Shape for Adenoviral Vector Vaccines. Vaccines (Basel) 2020; 8:vaccines8020155. [PMID: 32235634 PMCID: PMC7349762 DOI: 10.3390/vaccines8020155] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/20/2020] [Accepted: 03/26/2020] [Indexed: 12/20/2022] Open
Abstract
Vaccine dose-response curves can follow both saturating and peaking shapes. Dose-response curves for adenoviral vector vaccines have not been systematically described. In this paper, we explore the dose-response shape of published adenoviral animal and human studies. Where data were informative, dose-response was approximately five times more likely to be peaking than saturating. There was evidence that host species and response type may be sufficient for prediction of dose-response curve shape. Dose-response curve shape prediction could decrease clinical trial costs, accelerating the development of life-saving vaccines.
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.)
- Correspondence:
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.)
| | - Sara Afrough
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK (T.E.)
| | - Thomas Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK (T.E.)
| | - Richard White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.)
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27
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Afrough S, Rhodes S, Evans T, White R, Benest J. Immunologic Dose-Response to Adenovirus-Vectored Vaccines in Animals and Humans: A Systematic Review of Dose-Response Studies of Replication Incompetent Adenoviral Vaccine Vectors when Given via an Intramuscular or Subcutaneous Route. Vaccines (Basel) 2020; 8:E131. [PMID: 32192058 PMCID: PMC7157626 DOI: 10.3390/vaccines8010131] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/21/2022] Open
Abstract
Optimal vaccine dosing is important to ensure the greatest protection and safety. Analysis of dose-response data, from previous studies, may inform future studies to determine the optimal dose. Implementing more quantitative modelling approaches in vaccine dose finding have been recently suggested to accelerate vaccine development. Adenoviral vectored vaccines are in advanced stage of development for a variety of prophylactic and therapeutic indications, however dose-response has not yet been systematically determined. To further inform adenoviral vectored vaccines dose identification, historical dose-response data should be systematically reviewed. A systematic literature review was conducted to collate and describe the available dose-response studies for adenovirus vectored vaccines. Of 2787 papers identified by Medline search strategy, 35 were found to conform to pre-defined criteria. The majority of studies were in mice or humans and studied adenovirus serotype 5. Dose-response data were available for 12 different immunological responses. The majority of papers evaluated three dose levels, only two evaluated more than five dose levels. The most common dosing range was 107-1010 viral particles in mouse studies and 108-1011 viral particles in human studies. Data were available on adenovirus vaccine dose-response, primarily on adenovirus serotype 5 backbones and in mice and humans. These data could be used for quantitative adenoviral vectored vaccine dose optimisation analysis.
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Affiliation(s)
- Sara Afrough
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK;
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.); (J.B.)
| | - Thomas Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK;
| | - Richard White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.); (J.B.)
| | - John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; (S.R.); (R.W.); (J.B.)
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28
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Duthie MS, Frevol A, Day T, Coler RN, Vergara J, Rolf T, Sagawa ZK, Marie Beckmann A, Casper C, Reed SG. A phase 1 antigen dose escalation trial to evaluate safety, tolerability and immunogenicity of the leprosy vaccine candidate LepVax (LEP-F1 + GLA–SE) in healthy adults. Vaccine 2020; 38:1700-1707. [DOI: 10.1016/j.vaccine.2019.12.050] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/20/2019] [Indexed: 12/31/2022]
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29
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Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med 2019; 2:115. [PMID: 31799423 PMCID: PMC6877584 DOI: 10.1038/s41746-019-0193-y] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/01/2019] [Indexed: 12/12/2022] Open
Abstract
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
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Affiliation(s)
- Mark Alber
- Department of Mathematics, University of California, Riverside, CA USA
| | | | - William R. Cannon
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, WA USA
| | - Suvranu De
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY USA
| | | | - Krishna Garikipati
- Departments of Mechanical Engineering and Mathematics, University of Michigan, Ann Arbor, MI USA
| | | | - William W. Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY USA
| | - Paris Perdikaris
- Department of Mechanical Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Linda Petzold
- Department of Computer Science and Mechanical Engineering, University of California, Santa Barbara, CA USA
| | - Ellen Kuhl
- Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA USA
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