Naslov (srp)

Ispitivanja uticaja dizajna modela i parametara 3D štampe na brzinu rastvaranja lekovite supstance iz tableta dobijenih tehnikom deponovanja istopljenog filamenta : doktorska disertacija

Autor

Obeid, Samiha, 1995-

Doprinosi

Kovačević, Jovana, 1984-
Parojčić, Jelena, 1968-
Medarević, Đorđe, 1987-
Ibrić, Svetlana, 1971-

Opis (eng)

3D printing of drugs represents an advanced approach to provide personalized therapy according to theneeds of individual patients. The possibility of applying different 3D printing techniques, the selectionof suitable materials and overcoming existing challenges are the subject of intensive scientific research.The goal of this scientific research is to investigate the possibility of applying 3D printing technologyin the production of solid pharmaceutical forms obtained by Fused Deposition Modelling (FDM).Special attention was to examine the possibility of preparing filaments with diazepam and amlodipineas model drug substances by Hot-melt extrusion (HME) with the aim of using them as feeding materialin FDM 3D printing. The influence of model design and 3D printing parameters on the dissolution rateof drug substance from printed pharmaceutical forms was analyzed using advanced machine learningtools.By hot-melt extrusion (HME), it was possible to produce filaments with a uniform diameter, smoothsurface and suitable mechanical characteristics appropriate for 3D printing, using polyvinyl alcohol(PVA) as the base polymer, with and without the addition of sodium starch glycolate and/orhypromellose.Examining the effect of model design and 3D printing parameters on the rate of dissolution of drugsubstance from printed tablets showed that by optimizing the surface-to-volume ratio (SA/V) of printedobjects, infill density and infill pattern, a targeted drug substance release profile can be achieved.By changing the infill density, a change in the mass of the tablet is achieved, and thus the dose of theactive substance per tablet, without changing the dimensions of the tablet. This is of extremeimportance when adjusting the dose of the drug substance to the needs of the individual patient,because it allows to adjust the dose of the drug in tablets of the same shape and size, made of the samefilament, only by software settings and printing parameters. The fastest release of the drug wasachieved by using zigzag infill pattern, reducing the thickness of the tablet wall and with the addition ofsodium starch glycolate.Using self-organizing map (SOM) and multi-layer perceptron (MLP) neural network as advanced deeplearning tools, the influence of SA/V ratio and printing parameters (infill density and infill pattern) onthe release of diazepam from printed tablets was evaluated. The MLP was trained using the backpropagation algorithm and had three layers (with a 2-3-5 network structure). The obtained resultsshowed that a higher SA/V ratio, a lower infill density (less than 50%) and a zigzag infill pattern leadto a faster release of the drug substance. A comparison of the predicted and experimentally obtaineddiazepam dissolution profiles from the investigated formulations showed that the developed Artificialneural networks (ANN) model can successfully predict the drug release profile. The trained MLPnetwork enabled the establishment of a design space for formulated 3D printed tablets of diazepamwith the prediction of drug release kinetics depending on the infill density and the SA/V ratio, whichrepresents a significant scientific contribution of this research. In the case of amlodipine tablets, self-organizing maps (SOMs) were used to describe the influence of excipients and infill patterns on therelease of amlodipine from printed tablets. Self-organized maps showed that the fastest release ofamlodipine was achieved when the zigzag infill pattern was used, with the addition of sodium starchglycolate, while the addition of hypromellose did not significantly affect the dissolution rate ofamlodipine.

Opis (srp)

3D štampanje lekova predstavlja napredan pristup za obezbeđenje personalizovane terapije u skladu sapotrebama individualnih pacijenata. Mogućnost primene različitih tehnika 3D štampe, izbor pogodnihmaterijala i prevazilaženje postojećih izazova predmet su intenzivnih naučnih istraživanja. Cilj ovognaučnog istraživanja je ispitivanje mogućnosti primene tehnologije 3D štampe u proizvodnji čvrstihfarmaceutskih oblika dobijenih tehnikom deponovanja istopljenog filamenta (engl. Fused DepositionModelling, FDM). Posebna pažnja posvećena je ispitivanje mogućnosti pripreme filamenata sadiazepamom i amplodipinom kao model lekovitim supstancama tehnikom ekstruzije topljenjem (engl.Hot Melt Extrusion, HME) s ciljem njihove primene kao materijala za punjenje u 3D FDM štampi.Uticaj dizajna modela i parametara 3D štampe na brzinu rastvaranja lekovite supstance iz odštampanihfarmaceutskih oblika analiziran je primenom naprednih alata za mašinsko učenje.Metodom ekstruzije topljenjem (HME), uz primenu polivinil alkohola (PVA) kao osnovnog polimera,bez, kao i uz dodatak natrijum-skrobglikolata i/ili hipromeloze bilo je moguće izraditi filamenteujednačenog prečnika, glatke površine i odgovarajućih mehaničkih karakteristika pogodnih za 3Dštampu.Ispitivanje uticaja dizajna modela i parametara 3D štampe na brzinu rastvaranja lekovite supstance izštampanih tableta pokazalo je da se optimizacijom odnosa površine i zapremine (SA/V) štampanihobjekata, gustine punjenja i obrasca štampe može postići ciljani profil oslobađanja lekovite supstance.Promenom gustine punjenja postiže se promena mase tablete, a time i doza aktivne supstance potableti, bez promene dimenzija tablete. Ovo je od izuzetnog značaja prilikom prilagođavanja dozelekovite supstance potrebama individualnog pacijenta, jer omogućava da se isključivo podešavanjimasoftvera i parametara štampe podesi doza leka u tabletama istog oblika i veličine, izrađenim od istogfilamenta. Najbrže oslobađanje leka je postignuto korišćenjem cik-cak obrasca štampe, smanjenjemdebljine zida tablete i uz dodatak najtrijum-skrobglikolata.Primenom samoorganizovane mape (SOM) i neuronske mreže tipa višeslojnog perceptrona (MLP) kaonaprednih alata za duboko učenje procenjen je uticaj SA/V odnosa i parametara štampanja (gustinapunjenja i obrasca štampe) na oslobađanje diazepama iz štampanih tableta. MLP je obučen korišćenjemback propagation algoritma i imao je tri sloja (sa strukturom mreže 2-3-5). Dobijeni rezultati supokazali da veći SA/V odnos, manja gustina punjenja (manje od 50%) i cik-cak obrazac štampe dovodedo bržeg oslobađanja lekovite supstance. Poređenje predviđenih i eksperimentalno dobijenih profilarastvaranja diazepama iz ispitivanih formulacija pokazalo je da razvijeni veštačke neuronske mreže(engl. Artificial neural networks, ANN) model može da uspešno predvidi profil oslobađanja leka.Obučena MLP mreža je omogućila uspostavljanje prostora za dizajn (engl. design space) formulisanih3D štampanih tableta diazepama uz predviđanje kinetike oslobađanja leka u zavisnosti od gustinepunjenja i odnosa SA/V, što predstavlja značajan naučni doprinos ovog istraživanja. U slučaju tabletasa amlodipinom, samoorganizovane mape (SOM) su korišćene da se opiše uticaj ekscipijenasa iobrazaca štampe na oslobađanje amlodipina iz štampanih tableta. Samoorganizovane mape su pokazaleda je najbrže oslobađanje amlodipina postignuto kada su korišćeni cik-cak obrazac štampe, uz dodataknatrijum-skrobglikolata, dok dodatak hipromeloze nije značajno uticao na brzinu rastvaranjaamlodipina.

Opis (srp)

Farmacija - Farmaceutska tehnologija / Pharmacy- Pharmaceutical Technology Datum odbrane: 12.12.2022.

Jezik

srpski

Datum

2022

Licenca

Ovo delo je licencirano pod uslovima licence
Creative Commons CC BY-NC-ND 3.0 AT - Creative Commons Autorstvo - Nekomercijalno - Bez prerada 3.0 Austria License.

http://creativecommons.org/licenses/by-nc-nd/3.0/at/legalcode

Predmet

OSNO - Opšta sistematizacija naučnih oblasti, Farmaceutska tehnologija i kozmetologija

Personalizovani farmaceutski proizvodi, 3D štampane tablete, deponovanje istopljenog filamenta, obrazac štampe, gustina punjenja, diazepam, amlodipin, samoorganizovane mape, neuronske mreže, predviđanje brzine oslobađanja lekovite supstance

615.453.6.012:004.9(043.3)

OSNO - Opšta sistematizacija naučnih oblasti, Farmaceutska tehnologija i kozmetologija

Personalized pharmaceuticals, 3D printed tablets, fused deposition modelling, infill pattern, infill density, diazepam, amlodipine, self-organizing maps, neural networks, drug release prediction