In silico prediction of dissolution rates of pharmaceutical ingredients: Micro-kinetic model based on spiral dissolution
Başlık çevirisi mevcut değil.
- Tez No: 403060
- Danışmanlar: PROF. DR. KARSTEN REUTER
- Tez Türü: Doktora
- Konular: Kimya, Kimya Mühendisliği, Chemistry, Chemical Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2016
- Dil: İngilizce
- Üniversite: Technische Universität München
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 149
Özet
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Özet (Çeviri)
The quest to discover substances that have healing powers have always been an integral part of human life. The understanding of human body functions and response to the healing compounds open a new era for this discovery process. Not only the discovery but the development and design of what is nowadays known as drug compounds have started to be modeled in a systematic way. The most encountered drawback in the drug development process is that the drug candidates fail to reach to the targeted part in the body or even remain inactive mostly due to the poor bio-availability, hence being inadequate candidates. Due to an in-vivo-in-vitro correlation between dissolution rates and bio-availability of drug formulations, dissolution testing is considered as a potential tool for drug development and design process. However, the dissolution testing itself is time consuming and costly, requiring the drug formulations to be synthesized beforehand. On the other hand, an in silico method that is able to predict the dissolution rates accurately would greatly accelerate the drug development process and reduce the costs to take a new formulation to the market. To this end, in this work, a computational protocol is proposed to predict the dissolution rates of molecular crystals that found usages in pharmaceutical industry. Considering that organic crystals would dissolve along screw dislocations at low undersaturations, the classical spiral model of Burton-Cabrera-Frank (BCF) along with modifications as well as extensions is exploited. Other than readily available crystallographic parameters, only step velocities and critical lengths of step edges are required for the model. The step velocities are estimated considering the kink free energies of formation and detachment rate constants from the kink sites along the step edges formed by the screw dislocations on the crystal faces. As the estimation of the critical lengths of step edges also depends on kink free energies, only two parameters are necessary in addition to crystallographic constants. They are evaluated by accurate molecular dynamic (MD) simulation techniques. Then, the spiral dissolution model can be built to predict the dissolution rates of crystal faces. Depending on the interactions between molecules, different spiral shapes may be observed for the crystal faces. In this work, as test cases for the spiral dissolution model, two model organic crystals are considered: aspirin, an active pharmaceutical ingredient (API) and alpha-lactose monohydrate, a widely used excipient. The predicted dissolution rates for faces of aspirin and alpha-lactose monohydrate show good agreement with measured dissolution rates, around one order of magnitude differences are observed. By employing several approximations to the protocol developed, it is even possible to predict dissolution rates without extensive computational simulations. This renders the approach suitable for fast computational screenings of compounds used in drug formulations, and hence could notably reduce the time and cost of the drug development and design processes. The extensions of the protocol to other compounds would be more or less straightforward; however, the ability of the protocol to handle drug formulations that actually contain API and excipient together still remains to be tested.
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