Commercialization of Biopharmaceuticals

Biotekniikkaa pidetään yhtenä lupaavimmista nykyään tunnetuista teknologioista. Biotekniikan alalta erityisesti uusien lääkeaineiden kehittely on saavuttanut huomiota julkisuudessa. Biotekniikkaa lääkeaineiden kehittämiseen soveltavien yritysten määrä on kasvanut nopeasti viimeisen vuosikymmenen aikana, mutta tämänhetkiset tulokset osoittavat, että yritykset voisivat hyötyä riskien hallintaan ja kaupallistamiseen liittyvien prosessien kehittämisestä. Tutkielma keskittyy biolääkkeiden kaupallistamiseen, erityisesti suomalaisten uusien biolääkeyritysten kannalta. Tutkielma jakaantuu kahteen osaan: ensimmäinen osa tutkii kaupallistamista käsitteenä ja biolääkeliiketoiminnan erityispiirteitä. Toinen osa keskittyy kaupallistamisen empiiriseen tutkimukseen, joka kattaa viisi suomalaista uutta biolääkeyritystä. Empiirisen osan tavoitteena oli tunnistaa ne keinot, jotka auttavat menestyksekkään kaupallistamisprosessin luomisessa tuotekehitysvaiheen läpäisseelle lääkeaineelle. Saavutetut tulokset voidaan tiivistää neljän kriittisen menestystekijän ympärille, jotka ovat 1) tuote, 2) viestintä, 3) uskottavuus ja 4) yhteistyökumppanin valinta. Ensimmäinen menestystekijä on ainutlaatuinen biolääke, joka parantaa kansantaloudellisesti merkittäviä tauteja. Toisen menestystekijän avulla yritys viestittää uudesta ainutlaatuisesta tuotteestaan mahdollisille yhteistyökumppaneilleen. Kolmas menestystekijä kohdistuu yrityksen uskottavuuteen uutena korkean teknologian biolääkeaineiden kehittäjänä. Uskottavuustekijä on erityisen tärkeä suhteiden luomisessa kansainvälisiin lääkeyrityksiin. Neljäs tekijä keskittyy yhteistyökumppanin valintaan, joka alan erityisluonteesta johtuen on tärkeä uudelle biolääkeyritykselle. Viimeiseksi havaittiin, että uusi biolääkeyritys virtuaalisen rakenteensa vuoksi tarvitsee hyvät johdon suhdemarkkinointikyvyt.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Identifying New Business Opportunities and Customer Value Creation: Case Vuosaari

Previously conducted research projects in the field of logistics services have emphasized the importance of value added services in customer value creation. Through value added services companies can extend their service portfolio and gain higher customer satisfaction and loyalty. In more general level service marketing has been recognized to be challenging due the intangible nature of services. This has caused issues in pricing and value perceptions. To tackle these issues scholars have suggested well–managed customer reference marketing practices. The main goal of this research work is to identify shortages in the current service offering. Additionally, the focus is on, how these shortages can be fixed. Due the low capacity utilization of warehouse premises, there is a need to find the main factors, which are causing or affecting on the current situation. The research aims to offer a set of alternatives how to come over these issues. All the potential business opportunities are evaluated and the promising prospects are discussed. The focus is on logistics value added services and how those effect on route decisions in logistics. Simultaneously the aim is to create a holistic understanding of how added value and offered services effect on logistics centralization. Moreover, customer value creation and customer references’ effectiveness in logistics service marketing are emphasized in this project. Logistics value added services have a minor effect on logistics decision. Routes are chosen on a low–cost basis. However, it is challenging to track down logistics costs and break those down into different phases. Customer value as such is a difficult concept. This causes challenges when services are sold with value–based principles. Customer references are useful for logistics service providers and this should be exploited in marketing. Those reduce the perceived risk and give credibility to the service provider.