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Nirale P, Arora S, Solanki A, Bhat J, Singh RK, Yadav KS. Liquid filled hard shell capsules: Current drug delivery influencing pharmaceutical technology. Curr Drug Deliv 2021; 19:238-249. [PMID: 33645480 DOI: 10.2174/1567201818666210301094400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/31/2020] [Accepted: 01/05/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE Gastric absorption apparently is upfront route for drug delivery as it is convenient, economic and mostly suitable for getting the desired systemic effects. Unfortunately, many traditional and newer generation drugs suffer from poor solubility and thereby have lower bioavailability. With a perspective of bringing a novel delivery system in such condition for old/existing/new drugs, liquid filled hard capsules hold promise as delivery system. METHODS An organized state-of-the-art literature review including patents was done to accommodate information on the innovations in technology, processes and applications in the field of liquid filling in hard shell capsules. RESULTS The review findings revealed the importance of understanding the impact of liquid filled hard shell capsules would have in use of complex drug molecules specially the ones sensitive to light and moisture. This technology can have diverse functions to be used for both immediate and delayed drug release. Technology point of view the band sealing in such hard-shell capsules helps in providing protection against the tampering of the filled capsule. CONCLUSION The review gives an insight to the understanding of progression in the technology forefront related to formulation development of liquid formulations to be filled in hard shell capsules for better therapeutic potentials and convenience to the patients.
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Affiliation(s)
- Prabhuti Nirale
- ShobhabenPratapbhai Patel School of Pharmacy & Technology Management, SVKM's NMIMS (Deemed to be University), Mumbai. India
| | - Shivani Arora
- ShobhabenPratapbhai Patel School of Pharmacy & Technology Management, SVKM's NMIMS (Deemed to be University), Mumbai. India
| | | | | | - Rishi Kumar Singh
- ShobhabenPratapbhai Patel School of Pharmacy & Technology Management, SVKM's NMIMS (Deemed to be University), Mumbai. India
| | - Khushwant S Yadav
- ShobhabenPratapbhai Patel School of Pharmacy & Technology Management, SVKM's NMIMS (Deemed to be University), Mumbai. India
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Lai X, Geier OM, Fleischer T, Garred Ø, Borgen E, Funke SW, Kumar S, Rognes ME, Seierstad T, Børresen-Dale AL, Kristensen VN, Engebraaten O, Köhn-Luque A, Frigessi A. Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data. Cancer Res 2019; 79:4293-4304. [PMID: 31118201 PMCID: PMC7617071 DOI: 10.1158/0008-5472.can-18-1804] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 02/13/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022]
Abstract
The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes. SIGNIFICANCE: Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Oliver M Geier
- Department of Diagnostic Physics, Clinic of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Thomas Fleischer
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Øystein Garred
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Elin Borgen
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Simon W Funke
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Surendra Kumar
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Marie E Rognes
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Therese Seierstad
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Olav Engebraaten
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, University of Oslo, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
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Nair S, Kong ANT. Emerging roles for clinical pharmacometrics in cancer precision medicine. ACTA ACUST UNITED AC 2018; 4:276-283. [PMID: 30345221 DOI: 10.1007/s40495-018-0139-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Purpose of review Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the 'Cancer Moonshot' goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine. Recent findings Precision oncology has received federal funding largely due to 'The Precision Medicine Initiative'. Precision medicine takes into account the inter-individual variability, and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing "financial toxicity" to patients, families and caregivers. The National Cancer Institute (NCI) committed US$ 70 million from its fiscal year 2016 budget to advance precision oncology research. Through the 'Critical Path Initiative', pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability. Summary Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician's primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the Electronic Health Record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.
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Affiliation(s)
- Sujit Nair
- Amrita Cancer Discovery Biology Laboratory, Amrita Vishwa Vidyapeetham University, Amritapuri, Clappana P.O., Kollam - 690525, Kerala, India
| | - Ah-Ng Tony Kong
- Center for Cancer Chemoprevention Research and Department of Pharmaceutics, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ-08854, USA
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Barrette AM, Bouhaddou M, Birtwistle MR. Integrating Transcriptomic Data with Mechanistic Systems Pharmacology Models for Virtual Drug Combination Trials. ACS Chem Neurosci 2018; 9:118-129. [PMID: 28950062 DOI: 10.1021/acschemneuro.7b00197] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Monotherapy clinical trials with mutation-targeted kinase inhibitors, despite some success in other cancers, have yet to impact glioblastoma (GBM). Besides insufficient blood-brain barrier penetration, combinations are key to overcoming obstacles such as intratumoral heterogeneity, adaptive resistance, and the epistatic nature of tumor genomics that cause mutation-targeted therapies to fail. With now hundreds of potential drugs, exploring the combination space clinically and preclinically is daunting. We are building a simulation-based approach that integrates patient-specific data with a mechanistic computational model of pan-cancer driver pathways (receptor tyrosine kinases, RAS/RAF/ERK, PI3K/AKT/mTOR, cell cycle, apoptosis, and DNA damage) to prioritize drug combinations by their simulated effects on tumor cell proliferation and death. Here we illustrate a first step, tailoring the model to 14 GBM patients from The Cancer Genome Atlas defined by an mRNA-seq transcriptome, and then simulating responses to three promiscuous FDA-approved kinase inhibitors (bosutinib, ibrutinib, and cabozantinib) with evidence for blood-brain barrier penetration. The model captures binding of the drug to primary targets and off-targets based on published affinity data and simulates responses of 100 heterogeneous tumor cells within a patient. Single drugs are marginally effective or even counterproductive. Common copy number alterations (PTEN loss, EGFR amplification, and NF1 loss) have a negligible correlation with single-drug or combination efficacy, reinforcing the importance of postgenetic approaches that account for kinase inhibitor promiscuity to match drugs to patients. Drug combinations tend to be either cytostatic or cytotoxic, but seldom both, highlighting the need for considering targeted and nontargeted therapy. Although we focus on GBM, the approach is generally applicable.
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Affiliation(s)
- Anne Marie Barrette
- Department
of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New
York, New York 10029, United States
| | - Mehdi Bouhaddou
- Department
of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New
York, New York 10029, United States
| | - Marc R. Birtwistle
- Department
of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New
York, New York 10029, United States
- Department
of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29631, United States
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Analysis of copy number loss of the ErbB4 receptor tyrosine kinase in glioblastoma. PLoS One 2018; 13:e0190664. [PMID: 29342193 PMCID: PMC5771580 DOI: 10.1371/journal.pone.0190664] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 12/19/2017] [Indexed: 12/31/2022] Open
Abstract
Current treatments for glioblastoma multiforme (GBM)-an aggressive form of brain cancer-are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individual's tumor. Most receptor tyrosine kinases-such as EGFR-act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses prior to investing experimental resources.
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Abstract
Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.
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Affiliation(s)
- Jonathan R Dry
- Oncology Innovative Medicines and Early Development, AstraZeneca, R&D Boston, Waltham, MA, 02451, USA.
| | - Mi Yang
- Rheinisch-Westfälische Technische Hochschule Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, 52057, Germany
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. .,Rheinisch-Westfälische Technische Hochschule Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, 52057, Germany.
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Barbolosi D, Ciccolini J, Lacarelle B, Barlési F, André N. Computational oncology — mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 2015; 13:242-54. [DOI: 10.1038/nrclinonc.2015.204] [Citation(s) in RCA: 144] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Clegg LE, Mac Gabhann F. Molecular mechanism matters: Benefits of mechanistic computational models for drug development. Pharmacol Res 2015; 99:149-54. [PMID: 26093283 DOI: 10.1016/j.phrs.2015.06.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 06/06/2015] [Indexed: 12/19/2022]
Abstract
Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.
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Affiliation(s)
- Lindsay E Clegg
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States.
| | - Feilim Mac Gabhann
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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