Ahmadi Tehrani A, Omranpoor MM, Vatanara A, Seyedabadi M, Ramezani V. Formation of nanosuspensions in bottom-up approach: theories and optimization.
Daru 2019;
27:451-473. [PMID:
30661188 PMCID:
PMC6593134 DOI:
10.1007/s40199-018-00235-2]
[Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 12/17/2018] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND
Nanosuspensions, liquid dispersions with nanometer size distribution, are becoming trendy in pharmaceutical practice to formulate poorly water-soluble drugs and to enhance their bioavailability. Generally, nanosuspensions are produced in two main approaches; top-down or bottom-up. The former is based on size-reduction of large particles via milling or high pressure homogenization. The latter is focused on the mechanisms of nucleation and particle growth.
METHODS
In this review, the critical factors influencing the kinetics or dynamics of nucleation and growth are discussed. Subsequently, the mechanisms of nanosuspension instability as well as strategies for stabilization are elaborated. Furthermore, the effects of stabilizers on key parameters of instability as well as the process of choosing an appropriate stabilizer is discussed.
RESULTS
Steric and electrostatic stabilizations or combination of them is essential for nanosuspensions formulation to prevent coagulation. Accordingly, some characteristics of stabilizers play critical role on stability and optimization of nanosuspensions; i.e., HLB and concentration. Nevertheless, after reviewing various articles, it is ascertained that each formulation requires individual selection of surfactants according to the parameters of the particle surface and the medium.
CONCLUSIONS
Based on the results, application of excipients such as stabilizers requires proper optimization of type and concentration. This implies that each formulation requires its own optimization process. Graphical Abstract ᅟ.
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