1
|
Bhandari A, Gu B, Kashkooli FM, Zhan W. Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy. J Control Release 2024; 370:721-746. [PMID: 38718876 DOI: 10.1016/j.jconrel.2024.05.004] [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: 02/04/2024] [Revised: 04/11/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.
Collapse
Affiliation(s)
- Ajay Bhandari
- Biofluids Research Lab, Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
| | - Boram Gu
- School of Chemical Engineering, Chonnam National University, Gwangju, Republic of Korea
| | | | - Wenbo Zhan
- School of Engineering, University of Aberdeen, Aberdeen, UK.
| |
Collapse
|
2
|
Piranfar A, Moradi Kashkooli F, Zhan W, Bhandari A, Saboury B, Rahmim A, Soltani M. Radiopharmaceutical transport in solid tumors via a 3-dimensional image-based spatiotemporal model. NPJ Syst Biol Appl 2024; 10:39. [PMID: 38609421 PMCID: PMC11015041 DOI: 10.1038/s41540-024-00362-4] [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: 09/01/2023] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Lutetium-177 prostate-specific membrane antigen (177Lu-PSMA)-targeted radiopharmaceutical therapy is a clinically approved treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Even though common practice reluctantly follows "one size fits all" approach, medical community believes there is significant room for deeper understanding and personalization of radiopharmaceutical therapies. To pursue this aim, we present a 3-dimensional spatiotemporal radiopharmaceutical delivery model based on clinical imaging data to simulate pharmacokinetic of 177Lu-PSMA within the prostate tumors. The model includes interstitial flow, radiopharmaceutical transport in tissues, receptor cycles, association/dissociation with ligands, synthesis of PSMA receptors, receptor recycling, internalization of radiopharmaceuticals, and degradation of receptors and drugs. The model was studied for a range of values for injection amount (100-1000 nmol), receptor density (10-500 nmol•l-1), and recycling rate of receptors (10-4 to 10-1 min-1). Furthermore, injection type, different convection-diffusion-reaction mechanisms, characteristic time scales, and length scales are discussed. The study found that increasing receptor density, ligand amount, and labeled ligands improved radiopharmaceutical uptake in the tumor. A high receptor recycling rate (0.1 min-1) increased radiopharmaceutical concentration by promoting repeated binding to tumor cell receptors. Continuous infusion results in higher radiopharmaceutical concentrations within tumors compared to bolus administration. These insights are crucial for advancing targeted therapy for prostate cancer by understanding the mechanism of radiopharmaceutical distribution in tumors. Furthermore, measures of characteristic length and advection time scale were computed. The presented spatiotemporal tumor transport model can analyze different physiological parameters affecting 177Lu-PSMA delivery.
Collapse
Affiliation(s)
- Anahita Piranfar
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | | | - Wenbo Zhan
- School of Engineering, King's College, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ajay Bhandari
- Biofluids Research Lab, Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
| | - Babak Saboury
- Department of Computational Nuclear Oncology, Institute of Nuclear Medicine, Bethesda, MD, USA
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.
| |
Collapse
|
3
|
Akalın AA, Dedekargınoğlu B, Choi SR, Han B, Ozcelikkale A. Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty. Pharm Res 2023; 40:501-523. [PMID: 35650448 PMCID: PMC9712595 DOI: 10.1007/s11095-022-03298-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 05/17/2022] [Indexed: 01/18/2023]
Abstract
Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.
Collapse
Affiliation(s)
- Ali Aykut Akalın
- Department of Mechanical Engineering, Middle East Technical University, 06531, Ankara, Turkey
| | - Barış Dedekargınoğlu
- Department of Mechanical Engineering, Middle East Technical University, 06531, Ankara, Turkey
| | - Sae Rome Choi
- School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, Indiana, 47907, USA
| | - Bumsoo Han
- School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, Indiana, 47907, USA.
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.
- Center for Cancer Research, Purdue University, 585 Purdue Mall, West Lafayette, Indiana, 47907, USA.
| | - Altug Ozcelikkale
- Department of Mechanical Engineering, Middle East Technical University, 06531, Ankara, Turkey.
| |
Collapse
|
4
|
Multiscale Modelling of Nanoparticle Distribution in a Realistic Tumour Geometry Following Local Injection. Cancers (Basel) 2022; 14:cancers14235729. [PMID: 36497210 PMCID: PMC9736186 DOI: 10.3390/cancers14235729] [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: 09/16/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
Abstract
Radiosensitizers have proven to be an effective method of improving radiotherapy outcomes, with the distribution of particles being a crucial element to delivering optimal treatment outcomes due to the short range of effect of these particles. Here we present a computational model for the transport of nanoparticles within the tumour, whereby the fluid velocity and particle deposition are obtained and used as input into the convection-diffusion equation to calculate the spatio-temporal concentration of the nanoparticles. The effect of particle surface charge and injection locations on the distribution of nanoparticle concentration within the interstitial fluid and deposited onto cell surfaces is assessed. The computational results demonstrate that negatively charged particles can achieve a more uniform distribution throughout the tumour as compared to uncharged or positively charged particles, with particle volume within the fluid being 100% of tumour volume and deposited particle volume 44.5%. In addition, varying the injection location from the end to the middle of the tumour caused a reduction in particle volume of almost 20% for negatively charged particles. In conclusion, radiosensitizing particles should be negatively charged to maximise their spread and penetration within the tumour. Choosing an appropriate injection location can further improve the distribution of these particles.
Collapse
|
5
|
Bhandari A, Jaiswal K, Singh A, Zhan W. Convection-Enhanced Delivery of Antiangiogenic Drugs and Liposomal Cytotoxic Drugs to Heterogeneous Brain Tumor for Combination Therapy. Cancers (Basel) 2022; 14:cancers14174177. [PMID: 36077714 PMCID: PMC9454524 DOI: 10.3390/cancers14174177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/21/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Although developed anticancer drugs have shown desirable effects in preclinical trials, the clinical efficacy of chemotherapy against brain cancer remains disappointing. One of the important obstacles is the highly heterogeneous environment in tumors. This study aims to evaluate the performance of an emerging treatment using antiangiogenic and cytotoxic drugs. Our mathematical modelling confirms the advantage of this combination therapy in homogenizing the intratumoral environment for better drug delivery outcomes. In addition, the effects of local microvasculature and cell density on this therapy are also discussed. The results would contribute to the development of more effective treatments for brain cancer. Abstract Although convection-enhanced delivery can successfully bypass the blood-brain barrier, its clinical performance remains disappointing. This is primarily attributed to the heterogeneous intratumoral environment, particularly the tumor microvasculature. This study investigates the combined convection-enhanced delivery of antiangiogenic drugs and liposomal cytotoxic drugs in a heterogeneous brain tumor environment using a transport-based mathematical model. The patient-specific 3D brain tumor geometry and the tumor’s heterogeneous tissue properties, including microvascular density, porosity and cell density, are extracted from dynamic contrast-enhanced magnetic resonance imaging data. Results show that antiangiogenic drugs can effectively reduce the tumor microvascular density. This change in tissue structure would inhibit the fluid loss from the blood to prevent drug concentration from dilution, and also reduce the drug loss by blood drainage. The comparisons between different dosing regimens demonstrate that the co-infusion of liposomal cytotoxic drugs and antiangiogenic drugs has the advantages of homogenizing drug distribution, increasing drug accumulation, and enlarging the volume where tumor cells can be effectively killed. The delivery outcomes are susceptible to the location of the infusion site. This combination treatment can be improved by infusing drugs at higher microvascular density sites. In contrast, infusion at a site with high cell density would lower the treatment effectiveness of the whole brain tumor. Results obtained from this study can deepen the understanding of this combination therapy and provide a reference for treatment design and optimization that can further improve survival and patient quality of life.
Collapse
Affiliation(s)
- Ajay Bhandari
- Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
- Correspondence: (A.B.); (W.Z.)
| | - Kartikey Jaiswal
- Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Wenbo Zhan
- School of Engineering, King’s College, University of Aberdeen, Aberdeen AB24 3UE, UK
- Correspondence: (A.B.); (W.Z.)
| |
Collapse
|
6
|
Löke DR, Helderman RFCPA, Franken NAP, Oei AL, Tanis PJ, Crezee J, Kok HP. Simulating drug penetration during hyperthermic intraperitoneal chemotherapy. Drug Deliv 2021; 28:145-161. [PMID: 33427507 PMCID: PMC7808385 DOI: 10.1080/10717544.2020.1862364] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Hyperthermic intraperitoneal chemotherapy (HIPEC) is administered to treat residual microscopic disease after debulking cytoreductive surgery. During HIPEC, a limited number of catheters are used to administer and drain fluid containing chemotherapy (41–43 °C), yielding heterogeneities in the peritoneum. Large heterogeneities may lead to undertreated areas, increasing the risk of recurrences. Aiming at intra-abdominal homogeneity is therefore essential to fully exploit the potential of HIPEC. More insight is needed into the extent of the heterogeneities during treatments and assess their effects on the efficacy of HIPEC. To that end we developed a computational model containing embedded tumor nodules in an environment mimicking peritoneal conditions. Tumor- and treatment-specific parameters affecting drug delivery like tumor size, tumor shape, velocity, temperature and dose were assessed using three-dimensional computational fluid dynamics (CFD) to demonstrate their effect on the drug distribution and accumulation in nodules. Clonogenic assays performed on RKO colorectal cell lines yielded the temperature-dependent IC50 values of cisplatin (19.5–6.8 micromolar for 37–43 °C), used to compare drug distributions in our computational models. Our models underlined that large nodules are more difficult to treat and that temperature and velocity are the most important factors to control the drug delivery. Moderate flow velocities, between 0.01 and 1 m/s, are optimal for the delivery of cisplatin. Furthermore, higher temperatures and higher doses increased the effective penetration depth with 69% and 54%, respectively. We plan to extend the software developed for this study toward patient-specific treatment planning software, capable of mapping and assist in reducing heterogeneous flow patterns.
Collapse
Affiliation(s)
- Daan R Löke
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Roxan F C P A Helderman
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands
| | - Nicolaas A P Franken
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands
| | - Arlene L Oei
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands
| | - Pieter J Tanis
- Department for Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Johannes Crezee
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - H Petra Kok
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
7
|
Moradi Kashkooli F, Soltani M, Momeni MM. Computational modeling of drug delivery to solid tumors: A pilot study based on a real image. J Drug Deliv Sci Technol 2021. [DOI: 10.1016/j.jddst.2021.102347] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
8
|
Moradi Kashkooli F, Soltani M, Rezaeian M, Meaney C, Hamedi MH, Kohandel M. Effect of vascular normalization on drug delivery to different stages of tumor progression: In-silico analysis. J Drug Deliv Sci Technol 2020. [DOI: 10.1016/j.jddst.2020.101989] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
9
|
Moradi Kashkooli F, Soltani M, Hamedi MH. Drug delivery to solid tumors with heterogeneous microvascular networks: Novel insights from image-based numerical modeling. Eur J Pharm Sci 2020; 151:105399. [PMID: 32485347 DOI: 10.1016/j.ejps.2020.105399] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/27/2020] [Accepted: 05/26/2020] [Indexed: 12/14/2022]
Abstract
The present study examines chemotherapy by incorporating multi-scale mathematical modeling to predict drug delivery and its effects. This approach leads to a more-realistic physiological tumor model than is possible with previous approaches, as it obtains the capillary network geometry from an image, and also considers the tumor's necrotic core, drug binding, and cellular uptake. Modeling of the fluid flow and drug transport is then performed in the extracellular matrix. The results demonstrate a 10% drop in the fraction of killed cancer cells 69% rather than the 79% reported earlier for a tumor of similar geometry a more-accurate value. This study examines how tumor-related parameters including the necrotic core size and tumor size, and also drug-related parameters drug dosage, binding affinity of drug, and drug degradation can affect the delivery of the drug to solid tumors. Results indicate that concentration of drug are high in the tumor, low in normal tissue, and remarkably low in the necrotic core. Results also offer a treatment of tumors with smaller necrotic core. Tumor size, which implies the tumor progression, has a considerable impact on treatment outcomes, so to be more effective, treatment should be applied at a specific size of tumor. It is demonstrated that binding affinity of drugs to cell-surface receptors and drug dosage have significant impact on treatment efficacy, so they should be regulated based on a balanced quantification between maximum treatment efficacy and minimum side effects. On the other hand, considering the effects of drug degradation in the model has not significant effect on treatment efficacy. The findings of the present study provide insight into the mechanism of drug delivery to solid tumors based on analyzing the effective parameters and modeling how their behavior in the tumor microenvironment affects treatment efficacy.
Collapse
Affiliation(s)
- Farshad Moradi Kashkooli
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
| | | |
Collapse
|
10
|
Comparison of transport of chemotherapeutic drugs in voxelized heterogeneous model of human brain tumor. Microvasc Res 2019; 124:76-90. [PMID: 30923021 DOI: 10.1016/j.mvr.2019.03.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 02/26/2019] [Accepted: 03/11/2019] [Indexed: 01/20/2023]
Abstract
Systemic administration of chemotherapeutic drugs is widely used in the treatment of cancer. However, a good understanding of drug transport barriers that influence the treatment efficacy is still lacking. In this study, a voxelized numerical model based on dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and computational fluid dynamics (CFD) is employed to study the transport and efficacy of three different chemotherapeutic drugs, namely methotrexate, doxorubicin and cisplatin in human brain tumors. DCE-MRI data provides realistic heterogeneous vasculature of the tumor, the permeability of tissue to contrast agent, interstitial volume fraction (porosity) of the tissue and patient-specific arterial input function (AIF). The permeability of tissue to aforementioned drugs is determined by correlating it with the permeability of tissue to the contrast agent. The model is employed to simulate drug concentration in the tissue and compare the effect of heterogeneous vasculature on the distribution of the drugs in the tumor. The drug accumulation is observed to be higher in high permeability areas initially, and in higher porosity areas at later times. Furthermore, it is observed that methotrexate remains in the interstitial space of the tumor in higher concentration for a longer duration as compared to other two drugs, facilitating more tumor cell killing.
Collapse
|
11
|
Moradi Kashkooli F, Soltani M, Rezaeian M, Taatizadeh E, Hamedi MH. Image-based spatio-temporal model of drug delivery in a heterogeneous vasculature of a solid tumor - Computational approach. Microvasc Res 2019; 123:111-124. [PMID: 30711547 DOI: 10.1016/j.mvr.2019.01.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 12/31/2022]
Abstract
The solute transport distribution in a tumor is an important criterion in the evaluation of the cancer treatment efficacy. The fraction of killed cells after each treatment can quantify the therapeutic effect and plays as a helpful tool to evaluate the chemotherapy treatment schedules. In the present study, an image-based spatio-temporal computational model of a solid tumor is provided for calculation of interstitial fluid flow and solute transport. Current model incorporates heterogeneous microvasculature for angiogenesis instead of synthetic mathematical modeling. In this modeling process, a comprehensive model according to Convection-Diffusion-Reaction (CDR) equations is employed due to its high accuracy for simulating the binding and the uptake of the drug by tumor cells. Based on the velocity and the pressure distribution, transient distribution of the different drug concentrations (free, bound, and internalized) is calculated. Then, the fraction of killed cells is obtained according to the internalized concentration. Results indicate the dependence of the drug distribution on both time and space, as well as the microvasculature density. Free and bound drug concentration have the same trend over time, whereas, internalized and total drug concentration increases over time and reaches a constant value. The highest amount of concentration occurred in the tumor region due to the higher permeability of the blood vessels. Moreover, the fraction of killed cells is approximately 78.87% and 24.94% after treatment with doxorubicin for cancerous and normal tissues, respectively. In general, the presented methodology may be applied in the field of personalized medicine to optimize patient-specific treatments. Also, such image-based modeling of solid tumors can be used in laboratories that working on drug delivery and evaluating new drugs before using them for any in vivo or clinical studies.
Collapse
Affiliation(s)
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada; Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mohsen Rezaeian
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Erfan Taatizadeh
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | | |
Collapse
|