Makine Öğrenmesi ile Hedefe Yönelik Nanoterapötiklerin Üretim Parametrelerinin Optimizasyonu

Öz

Etkin ilaç dağıtımı, daha güvenli ve verimli terapötik sonuçlar elde etmek için bir ilacın belirli bir hastalık bölgesine verilmesini ifade etmektedir. Son zamanlarda geleneksel ilaç dağıtım yöntemleri yerine ilacın etkin bir şekilde dağıtımını hedefleyen nanopartikül temelli ilaç dağıtım yöntemleri kullanılmaktadır. Etkin ilaç dağıtımını sınırlandıran nanoparçacığın yapısından ve farmakokinetik sınırlamalardan kaynaklı çeşitli faktörler bulunmaktadır. Bu faktörler nanoterapötiklerin özellikle hastalıklı kısımlarda başarılı bir şekilde birikmesini engeller ve hastalık sürecinde etkin yanıtı sınırlar. Nanoterapötik ilaçların vücuttaki dağılımında karşılaşılan temel problem, nanopartiküllerin hastalık bölgelerinde terapötik ilaç seviyelerine ulaşamamasıdır. Spesifik bir hedefe yapılamayan ilaç dağıtımı ve terapötiklerin hastalıklı bölgede yetersiz birikimi gibi engeller, ilaç geliştiricileri için zorluk olmaya devam etmektedir. İlaç dağıtımına yönelik bu engelleri başarılı şekilde aşmak için geleneksel nanoparçacıkların yeniden tasarlanılmasına ihtiyaç vardır. Nanoparçacıkların intravenöz uygulamada karşılaştığı engeller göz önüne alındığında, hedefe yönelik terapötiklerin birikimini, kandaki dolaşım süresini ve hücre membran etkileşimlerini etkileyen birçok faktör olduğu anlaşılmaktadır. Bu parametrelerden başlıcaları; nanoparçacık yüzey yapısı ve yüzey yükü, nanoparçacık boyutu, nanoparçacık şekli, nanoparçacık uygulama yöntemi ve dozudur. Bu parametreler akciğer, karaciğer, dalak ve böbrekler dahil olmak üzere farklı organlar arasındaki biyolojik dağılımı belirler. Üretilen terapötik nanoparçacıkların farmakokinetik özelliklerinin belirlenmesi, uzun zaman isteyen, yüksek teknoloji ve uzmanlık gerektiren, yüksek maliyetli testlerle mümkün olmaktadır. Bu durum ar-ge aşamasında olan çalışmalar için hem zaman hem maliyet açısından ciddi bir engel oluşturmaktadır. Bu çalışmada biyolojik dağılımı etkileyen parametrelerle ilgili yapılan çalışmalardan elde edilen veriler ele alınarak nanotıp, ilaç sektörü, biyoteknoloji gibi birçok bölümde ilerde yapılacak çalışmalar için bir veri seti oluşturulmuştur. Ayrıca istenen özelliklere göre üretilecek olan nanoparçacığın üretim parametrelerinin optimum şekilde hesaplanmasını sağlayacak akıllı bir sistem tasarlanmıştır.

Anahtar Kelimeler

• Nanopartikül
• Nanoterapötik
• Makine Öğrenmesi
• Veri Seti
• Optimizasyon

Optimizing Production Parameters of Targeted Nanotherapeutics with Machine Learning

Öz

Effective drug delivery refers to the delivery of a drug to a specific disease site to achieve safer and more efficient therapeutic results. Recently, nanoparticle-based drug delivery methods aiming at effective drug delivery have been used instead of traditional drug delivery methods. There are several factors that limit the effective drug delivery due to the structure of the nanoparticle and pharmacokinetic limitations. These factors prevent the successful deposition of nanotherapeutics, especially in diseased parts, and limit the effective response in the disease process. The main problem encountered in the distribution of nanotherapeutic drugs in the body is that nanoparticles cannot reach therapeutic drug levels at disease sites. Barriers such as inability to deliver drugs to a specific target and insufficient accumulation of therapeutics at the diseased site remain challenges for drug developers. To successfully overcome these barriers to drug delivery, traditional nanoparticles need to be redesigned. Given the barriers nanoparticles face in intravenous administration, it appears that there are many factors that influence the accumulation of targeted therapeutics, circulation time in the blood, and cell membrane interactions. The main ones among these parameters are; nanoparticle surface structure and surface charge, nanoparticle size, nanoparticle shape, nanoparticle application method and dose. These parameters determine the biodistribution between different organs, including the lung, liver, spleen, and kidneys. Determination of the pharmacokinetic properties of the produced therapeutic nanoparticles is possible with high cost tests that require long time, high technology and expertise. This situation creates a serious obstacle in terms of both time and cost for studies that are in the R&D stage. In this study, a data set was created for future studies in many departments such as nanomedicine, pharmaceutical industry, biotechnology, by considering the data obtained from the studies on the parameters affecting the biological distribution. In addition, an intelligent system has been designed to optimally calculate the production parameters of the nanoparticle to be produced according to the desired properties.

Anahtar Kelimeler

• Nanoparticle
• Nanotherapeutic
• Machine Learning
• Dataset
• Optimization

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