An elicitation instrument for operationalising GQM+Strategies (GQM+S-EI) Abstract A recent approach for measurement program planning, GQM + Strategies, provides an important extension to existing approaches linking measurements and improvement activities to strategic goals and ways to achieve these goals. There is a need for instruments aiding in eliciting information from stakeholders to use GQM + Strategies. The success of GQM + Strategies highly depends on accurately identifying goals, strategies and information needs from stakeholders. The research aims at providing an instrument (called GQM + S-EI), aiding practitioners to accurately elicit information needed by GQM + Strategies (capturing goals, strategies and information needs). The research included two phases. In the first phase, using action research method, the GQM + S-EI was designed in three iterations in Ericsson AB. Thereafter, a case study was conducted to evaluate whether the information elicited with the designed instrument following the defined process was accurate and complete. We identified that the industry requires elicitation instruments that are capable to elicit information from stakeholders, not having to know about the concepts (e.g. goals and strategies). The case study results showed that our proposed instrument is capable of accurately and completely capturing the needed information from the stakeholders. We conclude that GQM + S-EI can be used for accurately and completely eliciting the information needed by goal driven measurement frameworks. The instrument has been successfully transferred to Ericsson AB for measurement program planning. In today's competitive software development market, there is an increasing need for having a well-structured measurement program for software organizations to understand, control and improve the current status of software processes, products and resources.. A well structured measurement program gives organizations the ability to follow up on goals at different levels of the organization by translating business strategies to the set of operational goals and break them down to respective measurements needed to evaluate them (Basili et al. 2007 . The key success for measurement initiatives is to provide a welldefined plan for executing measurement programs, which can be efficiently implemented afterwards (Mandic et al. 2010b) . As Mendonça and Basili (2000) point out, a high quality software measurement program should lead to sound, complete, lean, and consistent measurements.. A number of frameworks, models and standards (cf. Basili et al. 2010; Tahir and Jafar 2011) have been developed to support software organizations in planning their measurement programs. The main idea of many approaches is to provide a set of metrics that might be relevant for a specific application domain, process area, or quality aspect. Links to goals or links to information needs are typically only vaguely provided if at all. Mechanisms to creating such links are usually missing. However, there are a few exceptions, that aim at deriving measures from measurement goals (such as Goal Question Metric (Basili and Weiss 1984; Caldiera and Rombach 1994) ) or connecting measures to organizational goals (such as Balanced Score Card (Kaplan et al. 1996) ).. The empirical evidence collected in different contexts have shown that goal-driven measurement approaches are rigorous, adaptable and flexible (Boyd 2005) . Among these, some of the well-known ones are Goal Question Metric (GQM) (Basili and Weiss 1984; Caldiera and Rombach 1994) , Balanced Score Card (Kaplan et al. 1996) , Goals Questions Indicators Measures (GQ(I)M) (Boyd 2002) , Model Measure Manage Paradigm (M3P) (Offen and Jeffery 1997) , ISO/IEC 15939 standard on software measurement process (ISO/IEC15939 2002) and Decision Maker Model (McGarry et al. 2002) .. The Goal-Question-Metric (GQM) served the industry for quite a long time with the purpose of improving the process and quality of software product development (Ardimento et al. 2006) . GQM suggested that measurements in software development should be driven by measurement goals in a top down fashion. GQM defines a measurement model consisting of multiple levels: goals, questions and metrics. Measurement goals represent what should be achieved with the measurement. Questions represent the information needs of the stakeholders (i.e. answers to the questions help to achieve the measurement goal), and the metrics help in answering the questions. In this approach, each metric connects indirectly to a goal through one or more question(s), therefore no metrics are defined which do not have any justification related to a measurement goal.. GQM has been extended and complemented over time. Offen and Jeffery (1997) introduced the M 3 P which proposed a well-defined linkage among the numerical data and related development and business context by coupling business, organizational and technical issues into a measurement program context. M 3 P hence incorporates business goals, and emphasizes linking numerical results to them similar to GQ(I)M (Boyd 2002; Park et al. 1996) . GQ(I)M is the acronym for Goal Question Indicator Measure and the "I" in parenthesis emphasizes the difference between this framework and GQM. GQ(I)M extends GQM with different steps, for example the incorporation of business goals and actions to be taken to implement the measures. Furthermore, indicators (i.e. definitions of charts and visualizations) play an important role. In comparison to GQM, GQ(I)M incorporates business goals in addition to project goals, hence incorporating the business perspective (Boyd 2002) . It was specifically designed for the CMM/CMMI family of process improvement models (Park et al. 1996) .. Recently, Basili et al. (2007 Basili et al. ( , 2010 introduced GQM + Strategies to explicitly link organizational goals across multiple levels (e.g. business, software, and process). This model extends the GQM by introducing business goals to which measurement and operational goals are linked. With this, the GQM-approach gains a long-term perspective and hence the measurement program is more likely to be sustainable. Furthermore, strategies for how to reach the goals are incorporated to be able to follow up on whether a strategy was successful in reaching the business goals. Given the benefit of sustainability of the measurement programs based on GQM + Strategies, we chose this approach to form the basis for this research.. On the other hand, in order to run goal-driven measurement frameworks, plenty of information has to be elicited; including goals on multiple levels, strategies, and information needs. In particular, capturing all this information from the perspective of stakeholders accurately is essential, given that they will use the information as a decision parameter for metrics selection. The amount of information to be captured also increases when the scale of the organization is large (e.g. in terms of number of systems, or the number of people or projects involved). The different perspectives (e.g. roles in the organization) then have to agree on the accuracy of the captured information to achieve a common understanding, and to decide what is important (e.g. through walkthroughs or discussion). Alternatives for prioritizing the importance of goals and strategies and how to use that for selecting software metrics is discussed in Graham (2009) . However, the existing goal-driven approaches neither explicitly define how to, nor provide supporting guidelines for the information elicitation process. Kilpi (2001) briefly mentioned the overhead costs due to time consuming goals setting sessions and negotiations in Nokia. Boyd (2005) , referring to Wilson et al. (2002) , claimed that it is difficult to elicit information on business goals and strategies in an organisation as stakeholders were not always fully aware of, or able to articulate their goals. The need to arrive at a common understanding and agreement on what is important further adds to that challenge.. We checked whether elicitation approaches are available and were empirically evaluated in order to capture information needed for goal-driven measurement through a pilot search. The goal was to determine whether there exists any study where the main focus was on how to design an elicitation instrument for goal-based measurement program planning. The terms used were "(GQM OR goal based measurement) AND elicitation". The majority of the papers (over 500) returned were related to requirements engineering, but none was empirically evaluating and comparing approaches to elicit information for a measurement program in general, and GQM + Strategies in particular. In Park et al. (1996) , some guidelines are provided for eliciting information when applying goal-driven measurement process. However, no empirical evidence was provided, which the guidelines were built upon. Hence, we identified the following research gap: For goal-based measurement program planning we know what to collect, but have little empirical evidence for how to best collect the required information.. With the purpose of aiding organizations in eliciting information needed for GQM + Strategies based measurement program planning, we fulfilled the following objectives:. 1. Design an elicitation instrument for GQM + Strategies in close collaboration with industry and practically apply it. 2. Empirically evaluate the approach with respect to its ability to accurately capture information for GQM + Strategies from practitioners.. The remainder of this paper is structured as follows: Section 2 provides background on GQM + Strategies and the studies conducted on the topic. Thereafter, Section 3 presents the related work on information elicitation approaches in general, and in the context of goal-based measurement. Section 4 provides the research methodology used in this study. Section 5 presents the results, followed by a discussion of the results and their implications in Section 6. Section 7 concludes the paper. This research focused on developing an elicitation instrument to completely and accurately identify GQM + Strategies elements from measurement stakeholders, and then from that build GQM + Strategies grid. The research was conducted in two phases. In the first phase, the elicitation instrument was iteratively developed using action research. In the second phase, the final version of the instrument was evaluated with respect to its ability to accurately and completely identify GQM + Strategies elements from stakeholders. Two research questions have been investigated in these two phases:. Action research question (RQ1): How should software organizations elicit goals and strategies as input for GQM + Strategies? After having studied documentation to familiarize with the improvement programs and company's terminology, six interviews were needed to identify a stable and workable elicitation instrument. Here are the main findings:. -Semi-structured interviews using mind-mapping is preferred over templates and notations for elicitation purposes. Templates require deep knowledge about measurement terminology and information is added sequentially in tables hindering to get an overall overview. Notations also require knowledge about the measurement concepts. Questions not using the terminology when eliciting (e.g. goals and strategies) fared better as practitioners focus on providing insights of the improvement program in their domain during the interviews rather than struggling with the terminology. -Notations and templates are important from the measurement analysts' point of view when translating the mind-maps into GQM + Strategies elements and grids. -Multiple roles are required for the interviews when applying the elicitation instrument.. The interview leader moderates the interview and asks the questions, the support interviewer interrupts and asks follow-up questions, and the scribe documents the results in the mind-map tool visible on a screen to the interviewee. That way a "real-time" member-checking is also conducted. -While the interview initially focused on eliciting and structuring goals and strategies, it was expanded to incorporate all elements needed to construct the GQM + Strategies grid.. Case study research question (RQ2): How accurate and complete is GQM + S-EI in capturing GQM + Strategies relevant information from stakeholders? Saturation (measure of completeness) occurred after the sixth of eight interviews, while saturation occurred earlier on for the high level GQM + Strategies elements. This indicates that the elicitation approach achieves good results in completeness. With respect to accuracy (member checking was used to measure) only few changes were requested, which were primarily related to reformulations, no structural changes or additions/deletions were needed. The main change was to reformulate the business goal to better reflect the value focus of lean software development (which was the focus of the measurement program). Hence, we conclude that the designed elicitation instrument accurately reflected the views of the stakeholders.. A number of future research studies would be of interest. First, the elicitation instrument should be investigated in different contexts (e.g. small and medium sized scales). This allows gaining an understanding of how much effort is required for gathering the data in measurement programs in different contexts (e.g. with small scale saturation might occur much earlier). Another interesting direction is how to structure the elicited information. For instance, for elicitation we proposed to utilize product line concepts, which was not applicable at that stage. However, to be able to analyze a measurement program with respect to consistency, product line concepts could be useful in later stages. Prior to using GQM + S-EI we investigated company documentation to gain an understanding of the company. This part was essential as part of the research (document analysis being an important part of triangulation), but should be considered as an important part of the elicitation approach itself. During the iterative design of the elicitation instrument, different alternatives have been explored. The template-based approach is sequential in adding GQM + Strategies element and does not provide an overall picture connecting the elements. The notation based approach directly focused the interviewees on the elements (goals, strategies, etc.) and their meaning. In particular, it was confusing when a strategy on one level becomes a goal on the next level, etc. Hence, there was a need to elicit information to identify the elements without using the GQM + Strategies terminology for the identified reasons, and at the same time provide a structural overview. For this purpose, mind-mapping served best. During the interviews, further information was added sequentially. While the process was initially focused on strategies and goals, it was expanded to incorporate information needs, and metrics as well. Overall, this indicates that, it is essential to allow the practitioners to talk freely in their own terminology during elicitation, and later have the measurement analysts, who are the experts in the concepts, conduct the mapping. During the process of structuring the grid, notation based approaches appear to be most valuable.. During the case study, we found out that our elicitation instrument was capable of accurately and completely eliciting goals and strategies. To determine the completeness and accuracy of information elicited we used saturation and member checking as measures. We saw that, higher level goals and strategies were elicited early on with very few interviews. More interviews were needed to reach a saturation state at the lower levels. Overall, this indicates that the elicitation instrument can quickly lead to a comprehensive picture. This result has been obtained by interviewing people from different business units, and also different roles and perspectives that took part in the same improvement program. Thus, in a smaller scale saturation might be reached sooner. An interesting research direction for the future is to investigate how many interviews are needed in different contexts (scale, domain, or processes, see e.g. Petersen and Wohlin 2009b) as this provides interesting insight when estimating the effort for measurement program planning. The result of such a study might vary also depending on the persons involved. When starting with persons on the project level high level goals might be missing early on. In case no high level goals are elicited, we would propose to group existing goals and suggest high level goals (e.g. measurement experts might have experience allowing them to do that), and negotiate them with the measurement stakeholders involved. The case study was also conducted in a situation where no GQM + Strategies grid has been available prior to the case study. For a case with an existing grid we suggest to conduct the interviews as described and complement the existing grid with newly obtained information (see Section 4.3.5 for a description of how to systematically add information). When looking at how much new information is added with new interviews it will become visible whether the existing grid still reflects the situation of the company.. The interview design was geared towards GQM + Strategies, containing questions explicitly focused on strategies. However, it also subsumes information required by other measurement programs (see related work in Section 3). Hence, the solution presented is not just a GQM + Strategies solution, but a solution for all goal-based measurement program planning approaches. More information was collected, but this was not considered an issue as the actual elicitation from the stakeholders appears to be cost-effective.. The cost-effectiveness is also positively influenced by the way that the instrument is designed, i.e. only the measurement analysts need training in the concepts of GQM + Strategies by asking for the information using more intuitive questions allowing to talk practitioners freely (see Section 5.1.4). However, the measurement analysts need to have very good knowledge as they have to transfer the information from the interview into the GQM + Strategies grid, which requires the accurate identification of GQM + Strategies elements.. When having elicited all information there is a need to prioritize and take decisions which metrics to collect. For that purpose, it is important to assure traceability back to the interviewees, which can be directly done in the GQM + Strategies grid by color coding it according to sources (see Fig. 5 ).. We also would like to highlight that it was essential to have multiple people involved during the interviews as well as when interpreting and structuring the GQM + Strategies grid. As recommended by Keele (2007) it is useful to involve multiple persons in tasks where interpretation is required to increase the quality, and hence trust in the end-result. Member checking based on a walkthrough was used to check whether the instrument accurately and completely captured the information for the set of people interviewed. The group should agree on a version of the grid; hence walkthroughs could become part of the elicitation activity in practical usage also (not just for research purposes) to achieve a shared understanding. In the next step, the practitioners have to ask questions such as: "Which goals are important?", "Which strategies are best suited to achieve the goals?", and "Which metrics (based on the previous two answers) should be collected?". Having provided an instrument to accurate instrumentation we recommend that negotiation and review should be a continuous process when adopting the instrument. In this section, the results of Phase I and Phase II following the research methodology described in the previous section are presented. Table 7 shows the three major versions of GQM + S-EI that were developed. Given that not all stakeholders were co-located at the development site where we had our work places, few interviews had to be conducted through video conference. The company, for that purpose, provided a video conference system where the participants can see each other, and also can share their screens. The majority of interviews were conducted in the customer responsiveness measurement program. Version 3.0 has been tried in three different improvement programs before the final release which was evaluated in the case study. The area with most research on elicitation in software engineering is associated with the requirements engineering activity. Two systematic reviews (Davis et al. 2006; Dieste and Juzgado 2011) have been conducted to aggregate evidence to identify which elicitation approach has been most effective.. In the systematic review by Davis et al. (2006) , interviews have been found to be most effective in terms of amount of information gathered. Based on vote counting (counting number of studies in favor or against a specific outcome), they found that structured interviews appear to be more effective than unstructured interviews, and that unstructured interviews are more effective than the think aloud technique. Another finding was that visual aids do not aid discovering new information, but help in focusing the discussion.. The review by Dieste and Juzgado (2011) analyzed studies on interview techniques which were categorized into interviewing, laddering, sorting, and protocol review techniques. The findings of this review also support that interviews appear to be the most effective technique for elicitation. The least effective technique was protocol analysis. Park et al. (1996) suggested using structured brainstorming, the Nominal Group Technique (Scholtes et al. 2003 ) and interviews for eliciting information when applying goal driven measurement.. As part of the GQM approach, GQM abstraction sheets are a means to conduct interviews to elicit GQM-based information. An abstraction sheet selects a unit of study, purpose, quality focus, and context for one specific goal. It is answered from a viewpoint. Four quadrants are to be filled in with the interviewee, namely quality focus (measured properties), variation factors (influencing factors on measured properties in the quality focus), baseline hypotheses (current situation with respect to measured properties), and variation hypotheses (effect variation factors will have on the properties in the quality focus) (Briand et al. 1996) . The approach has been successfully used in industry as part of the overall GQM-approach (cf. Van Latum et al. 1998) . The abstraction sheet is valuable in eliciting information building the GQM graph as part of GQM + Strategies. It has been evaluated as part of GQM, but has not been the main focus of an empirical study. 2.1 GQM + Strategies Structure and Concepts GQM + Strategies (Basili et al. 2007 is the most recent advancement in goal-driven measurement program planning. Figure 1 illustrates the structure of GQM + Strategies. The key elements of GQM + Strategies are goals, strategies, and assumptions.. Goals, strategies, and assumptions/contextual information is collected and linked with each other. These are collected on multiple levels, e.g. business level, software level, and project level and linked with each other across levels.. To avoid a disconnection between the business perspective and operational measures the GQM + Strategies elements are linked to GQM graphs to document how they could be quantified to determine the achievement of the goals documented in GQM + Strategies elements. For instance, one could learn whether a new strategy used was leading an improvement with respect to the desired business goals. For that it is important to determine an interpretation model, which states when a goal has been achieved. Münch and Heidrich (2008) provide definitions for the concepts used in GQM + Strategies. The definitions from Münch and Heidrich (2008) are provided in Table 1. 2.2 Studies on GQM + Strategies Mandic et al. (2010b) evaluated the GQM + Strategies approach in a Finnish company. They used Bloom's taxonomy to evaluate the level of understanding that practitioners have in using the approach. This reaches from level 1 (remember the concepts) up to level 6 (being able to create the best possible GQM + Strategies grid/graph). The levels in between those are level 2 (understanding GQM + Strategies), level 3 (being able to apply GQM + Strategies), level 4 (being able to analyze GQM + Strategies) and level 5 (being able to evaluate GQM + Strategies). People were given training in the approach, which should allow them to achieve level 3. Higher levels require practical experience. In the evaluation of the approach 12 people from the company participated. Overall, the majority of the participants remembered the concepts. For higher levels (2-4) they indicated that that they feel comfortable in reaching those step working together with an expert supervisor. The practitioners with their current knowledge level considered level 5 and 6 as not being doable. Mandic et al. (2010a) and Mandic and Basili (2010) extended the GQM + Strategies by introducing the value goals with the intent of analysing business goals. To this end, they integrated business value analysis (BVA), which provided a coupling of cost-benefit and risk analysis (value goals) with operationally measurable business goals. Overall, the idea is based on return on investment, i.e. the return is the value gained, and the investment is the cost in creating the value in the organization. An illustrative example was presented to understand the approach, but no empirical evaluation has been done yet. Mandic and Oivo (2010) proposed a tool that allows capturing context information for GQM + Strategies as well as goals and strategies. The tool was built to support information elicitation for the GQM + Strategies when creating the tree/grid for the measurement program. However, the paper does not provide information of how to elicit the information from stakeholders in order to capture them in the tool. In a small survey with 12 participants the researchers found that the participants agreed on the usefulness of the tool in structuring the GQM + Strategies elicitation.. An evaluation of GQM + Strategies in the military training domain was presented in Sarcià (2010) . In this study, the authors elaborated the extent the GQM + Strategies has to be modified in order to be applicable. GQM + Strategies defines multiple levels as business level, organizational level, and product level. These levels were translated into more generic ones to be applicable to other domains, namely strategic level, design level, and execution level. In addition, they evaluated the ease of applying this approach in terms of efficiency and effectiveness by applying it to military training to evaluate training expenses. The study showed that the approach could be transferred to different domains, but at the same time requires excellent understanding of the concepts.. GQM + Strategies has been applied in the Japan Aerospace Exploration Agency (Kaneko et al. 2011) . The outcome was in the main focus of the work. However, how the outcome was reached and which challenges/support the approach supported were not elaborated. Some lessons learned have been presented, namely: (1) clarify the relationship between different organizational units and organizational levels; (2) involve the people in the organization; and (3) when having used the approach, record components for goals and strategies and reuse them to reduce effort in running the measurement program. According to the authors, GQM + Strategies also needs to be extended to incorporate priorities of goals.. The authors in Trendowicz et al. (2011) provide a case study of applying GQM + Strategies in the case of the Japanese Information-Technology Promotion Agency (IPA) with the goal of identifying the degree with which projects are contributing to business goals, and with this information to align the projects. The work was motivated by the observation that alignment of activities with business goals is seldom accomplished. A project alignment matrix is then used to show which strategic goals exist, and which project and its related goals match those strategic goals. In the case study five projects and strategy goals have been identified. The researchers conclude that the approach helped to achieve traceability from strategies to project goals, and hence support to update project goals accordingly. Münch et al. (2013b) conducted a recent case study reflecting on their experiences when using GQM+Strategies. The study lasted for five months and was structured along several workshops. The main drivers for elicitation in this case were workshops and planning meetings held with company representatives. The study highlights several challenges: 1) Initial training is needed given that stakeholders do not all have prior knowledge of the GQM+Strategies concepts; 2) it is challenging to find a suitable entry point to start the investigation; and 3) understanding the organizational structure is a challenge given that roles are not always clearly defined. Key recommendations were given as: having representatives from multiple levels of the organization to participate; using the concept of saturation to determine stability, and using goal evaluation patterns which appeared as very useful. Münch et al. (2013a) conducted another investigation at the same company as in Münch et al. (2013b) with the goal of determining goal harmonization and alignment. The study presents revisions of goals for revisions of the GQM+Strategies grid. The findings indicated that during workshops only little new information was added to the grid. Hence, overall the evidence indicated that GQM+Strategies helps in alignment and harmonization of the goals of an organisation. Other positive side-effects mentioned was increased transparency.

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O


Run probabilities of seed-like patterns and identifying good transition seeds Transition seeds exhibit a good tradeoff between sensitivity and specificity for homology search in both coding and non-coding regions. However, identifying good transition seeds is intractable. This work studies the hit probability of high-order seed patterns. Based on our theoretical results, we propose an efficient method for ranking transition seeds for seed design and list good seeds in different Bernoulli sequence models. We have studied the run probabilities of a high-order pattern in the Bernoulli sequence model. It is a generalization of basic spaced seeds and transition seeds. We first establish a recurrence formula for computing the hit probability of a high-order pattern; then, we analyze asymptotically the hit probability. We also give a formula for computing the average distance between successive non-overlapping hits of a pattern. Most importantly, we establish a relationship between the hit probability and the average distance between successive non-overlapping hits. For future work, one interesting problem is how to generalize the study to higher-order Markov sequence models.. By applying the theoretical results mentioned above, we present an efficient algorithm for identifying good transition seeds. This algorithm can also be adopted to identify multiple transition seeds.. Finally, we list good transition seeds for six different Bernoulli models. The insight gained from our theoretical study and the list of good transition seeds can be a useful resource in guiding the selection of seeds in the developing practical applications of homology searches such as Zhou and Florea (2007) .

O O O O O O O O B I B I O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O


An EM algorithm for the estimation of parameters of bivariate generalized exponential distribution under random left censoring Abstract. We consider the four parameter bivariate generalised exponential distribution proposed by Kundu and Gupta (2009) and propose an EM algorithm to find the maximum likelihood estimators of the four parameters under random left censoring. A numerical experiment is carried out to discuss the properties of the estimators obtained iteratively. Gupta and Kundu (1999) introduced the Generalised Exponential (GE) distribution and studied its probabilistic properties. It is very useful in studying skewed lifetime data and hence provides an alternative to gamma or Weibull distributions. For a recent review, see Gupta and Kundu (2007) . This distribution is naturally suitable for modelling left censored data.. Left censored data arises often in medical studies. For example, in the study of epidemics, such as AIDS, the time of onset of infection is typically unknown. What is known is the time at which the patient reports to the doctor. We study the M.L.E.s of the parameters of BVGE distribution under random left censoring.. In section 2 we restate the joint distribution function and joint density of BVGE distribution and also write the likelihood function under random left censoring. We discuss the EM (conditional) algorithm in section 3 for finding the M.L.E.'s of the parameters iteratively. The findings of the numerical experiments are reported in section 4. One real data set is analysed in section 5 and we conclude in section 6. Details of the likelihood functions are given in Appendix A and the observed Fisher information matrix is in Appendix B.

O B I O O O O O O B I I I O B I I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


and

similarity

landscape

analysis

identifies

PARP

as

a

novel

off-target

for

the

drug

Vatalanib

Background:

Searching

for

two-dimensional

structural

similarities

is

a

useful

tool

to

identify

new

active

compounds

in

drug-discovery

programs.

However,

as

similarity

measures

neglect

important

structural

and

functional

features,

similarity

by

might

be

underestimated.

In

the

present

study,

we

used

combined

and

three-dimensional

similarity

comparisons

to

reveal

possible

new

functions

and/or

side-effects

of

known

bioactive

compounds.

Results:

We

utilised

more

than

compounds

from

the

SuperTarget

database

with

known

inhibition

values

for

twelve

different

anti-cancer

targets.

We

performed

all-against-all

comparisons

resulting

in

similarity

landscapes.

Among

the

regions

with

low

similarity

scores

are

inhibitors

of

vascular

endothelial

growth

factor

receptor

(VEGFR)

and

inhibitors

of

poly

ADP-ribose

polymerase

(PARP).

To

demonstrate

that

landscape

comparison

can

identify

similarities,

which

are

untraceable

in

similarity

comparisons,

we

analysed

this

region

in

more

detail.

This

analysis

showed

the

unexpected

structural

similarity

between

inhibitors

of

VEGFR

and

inhibitors

of

PARP.

Among

the

VEGFR

inhibitors

that

show

similarities

to

PARP

inhibitors

was

Vatalanib,

an

oral

"multi-targeted"

small

molecule

protein

kinase

inhibitor

being

studied

in

phase-III

clinical

trials

in

cancer

therapy.

An

in

silico

docking

simulation

and

an

in

vitro

HT

universal

colorimetric

PARP

assay

confirmed

that

the

VEGFR

inhibitor

Vatalanib

exhibits

off-target

activity

as

a

PARP

inhibitor,

broadening

its

mode

of

action.

Conclusion:

In

contrast

to

the

search,

the

landscape

comparison

identifies

new

functions

and

side

effects

of

the

known

VEGFR

inhibitor

Vatalanib.

similarity

landscape

comparison,

as

shown

in

this

study,

has

the

potential

to

identify

new

targets

of

known

drugs.

As

a

proof

of

principle,

we

identified

Vatalanib's

additional

ability

to

target

PARP,

which

was

demonstrated

in

vitro

and

in

vivo.

Thus,

combined

and

similarity

landscape

comparison

analysis

can

identify

new

functions

and/or

side

effects

of

known

bioactive

compounds

that

are

untraceable

with

similarity

searching

alone.

fingerprints

similarity

search

methods

are

widely

used

approaches

in

the

discovery

of

novel

molecules

with

high

affinity

to

specific

targets

and,

despite

the

fact

that

molecules

are

active

in

three

dimensions,

surprisingly

powerful

In

this

study

we

used

the

freely

available

software

packages

OpenBabel

and

ChemAxon

to

analyse

the

of

about

inhibitors

against

twelve

promising

anti-cancer

targets.

Among

these

VEGFR

and

PARP

inhibitors

showed

only

little

structural

similarity,

however,

similarity

by

might

be

underestimated

Accurate

target

prediction

can

be

achieved

by

combining

different

measures

of

chemical

similarity

based

on

both

chemical

structure

and

molecular

shape

Furthermore

it

has

been

shown

recently

that

the

combination

of

a

similarity

search

and

a

shape/flexibility-based

similarity

search

led

to

an

increased

hit

rate

Therefore

of

VEGFR

and

PARP

inhibitors

was

then

analysed

by

a

proprietary

algorithm,

which

produces

reproducible

results

because

of

pre-calculated

conformers

for

every

compound.

The

method

in

combination

with

comparison

performs

quite

well

by

applying

a

quantile

threshold

(corresponding

to

an

rmsd-value

of

for

early

discovery

detection.

This

is

possible

as

our

data

follows

a

normal

distribution.

Nevertheless,

using

a

threshold

means

missing

out

on

compounds

with

larger

rmsd-values

which

could

have

the

same

inhibitory

function.

By

taking

conformers

into

account

to

simulate

the

flexibility

and

to

cover

the

conformational

space,

superpositions

of

the

two

compounds

were

calculated

and

the

best

superposition

with

a

minimal

rmsd-value

was

taken

for

the

similarity

measurement.

This

makes

the

method

robust

with

respect

to

conformation

changes.

Although

the

landscape

of

the

shows

an

overall

reduced

similarity

of

inhibitors

compared

to

the

selected

PARP

inhibitors

display

high

similarity

when

compared

to

the

VEGFR

inhibitors,

confirming

that

it

is

important

to

take

in

addition

to

similarity

into

account

to

increase

the

hit

rate.

Among

the

inhibitors

with

similarity

we

found

the

anticancer

agent

Vatalanib,

and

Compound

a

similarity

not

identified

by

different

fingerprint

algorithms.

It

might

be

that

the

algorithms

failed

to

identify

this

similarity,

because

these

fingerprints

are

based

on

atom

labels

whereas

rmsd

does

not

consider

these

labels.

Structural

comparisons

as

performed

by

the

algorithm

used

here

might

perform

better

generally,

as

they

compensate

for

fragments

of

the

molecule

and

their

connections

of

different

atom

types.

Still,

there

might

be

clusters

where

similarity

algorithms

might

be

faster

and

better..

Compound

was

identified

as

a

direct

inhibitor

in

a

yeast

screen

assay

with

an

EC

of

approximately

μM

By

using

the

HT

universal

colorimetric

PARP

assay

kit

we

calculated

an

IC

value

of

approximately

μM.

The

differences

in

the

IC

values

of

Compound

can

be

explained

by

differences

in

the

assays

used

as

well

as

by

differences

of

PARP

expressed

in

yeast

compared

to

an

isolated

PARP

enzyme

used

in

our

assay.

According

to

the

colorimetric

PARP

assay,

Vatalanib,

with

an

IC

value

of

μM

is

fifteen-fold

more

effective

than

Compound

in

inhibiting

PARP.

The

in

silico

docking

simulation

indicates

that

Vatalanib's

additional

chloride

atom,

which

is

missing

in

Compound

is

in

close

proximity

to

the

arginine

at

the

bottom

of

the

binding

site

of

PARP.

A

halogen

bond

between

the

nitrogen

of

arginine

might

be

formed,

stabilising

the

binding

position,

which

results

in

a

more

effective

drug

target

inhibition..

Both

Vatalanib

and

Compound

have

higher

LibDock

docking

scores

than

which

might

be

attributed

to

the

relative

small

molecule

size

of

Despite

better

LibDock

docking

scores,

both

Vatalanib

and

Compound

are

less

potent

than

the

prototype

PARP

inhibitor

which

has

an

IC

of

about

μM

Despite

these

comparatively

high

IC

values

of

Compound

and

Vatalanib,

μM

of

either

of

the

drugs

was

able

to

induce

foci

formation

in

human

breast

cancer

cells,

demonstrating

DNA

damage

and

PARP

inhibition.

In

regard

to

this

activity,

these

results

again

point

to

an

in

vivo

vs.

in

vitro

discrepancy

with

a

higher

bioactivity

in

cells

compared

to

the

enzyme

assay.

Due

to

the

relative

high

IC

of

Vatalanib,

Vatalanib

might

be

of

interest

as

a

new

PARP

inhibitor

or

for

drug

design.

In

addition

a

putative

positive

side

effect

of

Vatalanib

against

PARP

might

be

important

for

the

use

of

Vatalanib

as

a

chemotherapeutical.

Vatalanib

doses

up

to

mg

twice-a-day

are

well

tolerated

reaching

plasma

concentration

in

patients

in

the

μM

range.

Accordingly,

the

μM

of

Vatalanib,

able

to

induce

foci,

is

in

the

rage

of

c

max

plasma

concentrations

achieved

in

patients

Vatalanib

was

initially

described

as

a

selective

tyrosine

kinase

inhibitor

(TKI)

of

TKIs

commonly

have

additional

activity

against

other

tyrosine

kinases.

Likewise

Vatalanib,

which

at

higher

concentrations

also

inhibits

other

protein

tyrosine

kinases

of

the

same

family,

such

as

the

platelet-derived

growth

factor

receptor

beta

tyrosine

kinase

and

the

c-Kit

protein

tyrosine

kinase

Interestingly,

activity

across

other

classes

of

drug

targets

have

also

been

documented

for

Vatalanib.

It

has

been

shown

that

Vatalanib

significantly

inhibits

aromatase

and

thus

might

cross-inhibit

two

important

classes

of

targets

in

breast

cancer

This

"multi-targeting"

activity,

which

might

also

include

PARP

as

a

target,

could

potentially

contribute

to

the

antitumor

effect

of

Vatalanib

and

indicates

that

a

drug's

efficacy

often

might

not

only

be

based

on

the

inhibition

of

one

target

but

of

multiple

targets

First,

we

performed

an

in

silico

screening

for

a

variety

of

known

inhibitors

against

twelve

promising

anti-cancer

targets

(listed

in

Fig.

using

our

SuperTarget

database

The

overall

of

about

inhibitors

was

then

displayed

in

heat-maps

(Additional

file

Figure

where

the

values

are

coloured

according

to

the

similarity

of

the

analysed

inhibitors

as

calculated

by

the

Tanimoto

score:

high

similarity

is

displayed

in

red

and

low

similarity

in

yellow

data

points.

To

allow

for

a

better

access

to

these

values,

we

represent

these

heat-maps

as

landscapes

of

similarity.

Here,

high

similarity

is

represented

by

Fig.

similarity

landscape.

similarity

landscape

of

about

inhibitors

for

the

twelve

anti-cancer

targets.

High

similarity,

calculated

by

the

Tanimoto

score,

is

visualised

by

mountains

(white-yellow)

and

low

similarity

by

valleys

(dark

red).

We

focused

on

the

similarity

between

PARP

and

VEGFR

inhibitors

(blue

square,

upper

left

corner

mountains

and

low

similarity

by

valleys

(see

Fig.

Within

each

group

and

in

between

groups,

inhibitors

display

similarity

as

indicated

by

high

Tanimoto

scores

depicted

by

ridges

and

mountains.

In

contrast,

the

VEGFR

inhibitors

showed

only

little

structural

similarity

to

the

other

classes

of

inhibitors,

but

especially

to

the

PARP

inhibitors

(Fig.

upper

left

corner)

Because

analyses

often

neglect

important

structural

and

functional

features,

we

expanded

our

comparison

to

measured

by

the

root-meansquare

deviation

(rmsd)

based

on

the

Kabsch

algorithm.

Although

current

computers

calculate

of

compounds

relatively

fast,

it

would

still

take

months

to

compare

all

inhibitors

with

each

other.

We

therefore

focused

on

the

comparison

of

VEGFR

and

PARP

inhibitors,

which

showed

only

little

structural

similarity

in

the

analyses.

For

this

comparison

up

to

conformers

were

calculated

by

using

Accelrys

Discovery

studio

(Accelrys

Software

Inc.,

Discovery

Studio

Modeling

Environment,

Release

San

Diego:

Accelrys

Software

Inc.,

To

create

diverse

ligand

conformations,

the

'fast'

search

method

was

used

to

generate

multiple

low-energy

conformations.

The

rmsd

calculations

are

based

on

overlaying

the

anchor

points

of

both

conformers

and

to

rotate

at

single

bonds

(degrees

of

freedom)

to

minimise

the

rmsd.

We

arbitrarily

chose

a

quantile

(rmsd

of

to

evaluate

the

similarity

of

related

structures

and

analysed

only

compounds

with

both

low

rmsd

(high

similarity)

and

low

Tanimoto

scores

(low

The

resulting

overall

similarities

of

VEGFR

and

PARP

inhibitors

were

again

displayed

in

heat-maps

and

as

landscapes

of

similarity

(Fig.

The

clearly

depicts

that

the

inhibitory

compounds

of

VEGFR

and

PARP

are

similar

within

each

group,

but

that

there

is

only

little

similarity

detectable

between

both

classes

of

inhibitors.

The

results

are

visualised

in

a

similar

heat-map

and

corresponding

landscape

that

shows

overall

reduced

similarity

of

inhibitors

within

each

group.

Nevertheless,

selected

PARP

inhibitors

display

high

similarity

when

compared

to

the

VEGFR

inhibitors.

Interestingly,

there

is

a

remarkable

similarity

between

some

inhibitors

of

both

classes

(rmsd

values

between

and

depicted

by

the

ridges,

which

cannot

be

seen

in

the

landscape,

thereby

proving

the

importance

of

additional

superposition

analysis..

Among

these

similar

inhibitors

(for

a

more

detailed

list

see

Additional

file

Table

we

found

Vatalanib

(N-(4-chlorophenyl)-4-(pyridin-4-ylmethyl)phthalazin-1-amine;

CID

an

approved

inhibitor

for

VEGFR,

which

showed

similarity

to

a

well-known

PARP

inhibitor

sulfanylphthalazine;

CID

hereinafter

referred

to

as

Compound

(Fig.

Based

on

the

fact

that

Vatalanib

is

a

well-known

drug,

we

decided

to

further

investigate

pair

of

compounds..

The

screening

identifies

Vatalanib

as

a

potential

inhibitor

of

PARP

(rmsd:

whereas

OpenBabel

fingerprints

calculated

a

low

Tanimoto

score

of

and

therefore

failed

to

recognise

the

similarity

of

these

two

compounds.

To

analyse

in

more

detail

if

other

To

verify

Vatalanib's

function

as

an

inhibitor

of

PARP,

we

first

performed

an

in

silico

docking

simulation

to

analyse

the

binding

of

both

compounds

into

the

active

site

of

PARP

(Fig.

The

docking

was

performed

by

using

the

PDB

structure.

PARP

catalyses

the

NAD-dependent

addition

of

poly

(ADP-ribose)

(PAR)

onto

various

cytoplasmic

and

nuclear

proteins,

and

PARP

inhibitors

are

thought

to

compete

with

the

enzyme

substrate

NAD+

at

the

active

site.

The

high

spatial

similarity

is

displayed

by

overlapping

both

compounds

as

well

as

docking

them

to

PARP.

By

using

the

high-throughput

docking

algorithm

LibDock

the

best

docking

positions

of

both

compounds

were

calculated.

In

addition

the

docking

score

of

was

calculated

as

a

reference.

According

to

the

integrated

scoring

function,

the

best-ranked

poses

for

Vatalanib

and

Compound

have

comparable

LibDock

scores

of

and

respectively,

showing

that

Vatalanib,

like

Compound

fits

neatly

into

the

active

site

of

PARP.

Both

structures

have

a

higher

docking

score

than

the

reference

structure

with

a

docking

score

of

for

the

best-suited

pose..

To

confirm

our

computational

hypothesis

that

Vatalanib

also

targets

PARP,

we

next

compared

the

IC

values

of

both

compounds.

IC

values

were

determined

by

the

use

of

the

HT

universal

colorimetric

PARP

assay

kit,

which

measures

the

incorporation

of

biotinylated

poly(ADPribose)

onto

histone

proteins.

Both

compounds

inhibited

PARP

activity

in

a

concentration-dependent

manner

with

IC

values

of

about

μM

and

about

μM

for

Compound

and

Vatalanib,

respectively

(Fig.

To

confirm

inhibition

of

PARP

and

to

rule

out

in

vivo

vs.

in

vitro

activity

discrepancies,

i.e.

differences

of

PARP

expression

in

cells

vs.

an

isolated

PARP

enzyme,

we

analysed

accumulation

of

DNA

damage

in

a

human

breast

Because

of

PARP's

involvement

in

DNA

strand

break

repair,

its

inhibition

has

been

proposed

to

lead

to

double-strand

break

(DSB)

formation

These

DSBs

induce

phosphorylation

of

histone

on

at

sites

flanking

the

breakage

Therefore,

we

analysed

whether

treated

cells

accumulate

phosphorylated

denoted

as

which

provides

a

common

marker

for

DNA

damage

in

vitro

cells

were

treated

with

increasing

concentrations

of

Compound

and

Vatalanib,

incubated

for

h

and

stained

for

Green

immunofluorescence

(Fig.

indicates

accumulation

of

foci

after

treatment

with

concentrations

of

and

μM

of

Compound

or

Vatalanib.

Treatment

with

either

μM

of

Compound

or

μM

of

Vatalanib

is

sufficient

to

cause

accumulation

of

foci,

indicating

accumulation

of

DNA

damage

resulting

from

PARP

inhibition.

Drugs

often

not

only

interact

with

their

intended

target

but

also

with

so-called

off-targets,

thereby

causing

sideeffects

Prediction

of

side-effects

is

still

a

big

challenge

during

drug

design

and

studies

have

shown

the

potential

of

computational

methods

for

target

and

off-target

analysis

These

studies

deal

with

pathway-and

networkbased

approaches

combined

with

the

chemical

structure

of

small

molecule

compounds

regarding

their

binding

site

at

the

target

protein

To

identify

compound

similarities

it

is

important

to

take

a

detailed

look

at

structural

similarity

algorithms

were

generated

to

predict

and

create

a

drug-target

adverse

drug

reactions

(ADR)

network

These

represent

the

structure

and

properties

of

small

molecules

by

a

bit

or

integer

string.

Although

several

methods

exist

to

measure

the

similarity

between

fingerprints,

the

Tanimoto

coefficient

has

been

proven

to

be

reliable

However,

several

problems

can

occur

while

working

with

fingerprints:

size

of

compounds

as

well

as

functional

groups

or

side

chains

have

an

impact

on

the

similarity

calculations.

Hence,

functional

and

structural

features

of

compounds

can

be

neglected.

These

problems

can

be

overcome

by

using

similarity

search

methods..

Non-commercial

drug-or

target-related

databases,

which

have

been

established

in

the

last

decade,

can

be

used

for

and

comparisons.

Millions

of

compounds

can

be

found

in

databases

like

ChEMBL

or

PubChem

and

their

availability

can

be

verified

via

the

ZINC

database

We

recently

established

our

SuperTarget

database,

which

was

developed

with

the

intention

to

accentuate

drug-target

interactions

and

to

provide

references

to

other

resources

for

more

elaborate

analysis

The

SuperTarget

database

contains

a

core

dataset

of

about

drug-target

interactions,

of

which

about

interactions

have

calculated

binding

affinity

data

and

were

used

to

compare

and

structures

of

promising

anticancer

drugs..

Among

these

drugs

are

inhibitors

of

the

poly

ADP-ribose

polymerase

(PARP).

PARP

binds

to

single-strand

DNA

breaks

and

plays

a

critical

role

in

cell

recovery

from

DNA

damage.

PARP

inhibitors

show

activities

not

only

in

cancer

therapy

but

are

also

being

evaluated

for

the

treatment

of

stroke,

myocardial

infarction

and

other

diseases.

Additional

promising

anticancer

drugs,

which

can

be

found

in

the

SuperTarget

database,

are

inhibitors

of

the

vascular

endothelial

growth

factor

receptor

(VEGFR).

The

approved

VEGFR

inhibitor

Vatalanib

or

PTK/ZK)

is

currently

studied

in

several

phases

of

clinical

trials

for

different

cancer

therapies

Vatalanib

is

an

oral

"multi-targeted"

small

molecule

protein

kinase

inhibitor

that

binds

to

the

intracellular

kinase

domain

of

all

VEGF

receptor

subtypes,

thereby

inhibiting

angiogenesis

In

addition,

it

binds

to

c-KIT

and

platelet-derived

growth

factor

receptor

(PDGFR)

but

with

lower

affinity..

While

applying

a

similarity

landscape

analysis

on

inhibitors

for

different

cancer

targets

by

using

the

SuperTarget

database,

we

found

unexpected

similarities

between

PARP

and

VEGFR

inhibitors,

which

could

not

be

detected

by

similarity

searches.

As

a

proof

of

concept

of

our

similarity

landscape

analysis,

both

in

silico

and

in

vitro

assays

confirmed

Vatalanib's

off-target

activity

as

a

PARP

inhibitor.

In

this

paper

we

provide

a

combined

approach

of

and

similarity

landscapes

for

target

and

off-target

analysis,

which

can

be

applied

to

a

larger

number

of

targeted

anti-cancer

therapeutics.

was

calculated

with

the

Tanimoto

coefficient

for

pairs

of

compounds,

i.e.

inhibitors

For

many

of

these

inhibitors

the

half-maximal

inhibitory

concentration

(IC

effective

concentration

(EC

or

dissociation

constant

(Kd)

values

are

listed

in

the

SuperTarget

database.

These

known

affinities

of

the

inhibitors

were

the

basis

of

our

filter

algorithm

and,

to

avoid

unwanted

and

therefore

incomputable

off-target

effects,

only

interactions

described

by

binding

affinities

μM

(IC

EC

or

Kd)

were

considered

for

further

analysis.

Using

this

filter

method,

we

identified

nearly

inhibitors

for

the

twelve

different

anticancer

targets.

To

compare

these

inhibitors,

a

combination

of

fingerprint

and

fingerprint

were

calculated.

is

used

to

compare

small

molecules;

it

links

linear

segments

of

a

fragment

up

to

seven

atoms

to

an

index

and

considers

atoms

and

bonds

of

a

fragment

and

whether

a

complete

ring

exists.

Based

on

these

calculations,

fragments

are

assigned

to

set

bits

in

a

bit

vector.

uses

SMART

patterns

of

functional

groups

of

the

small

molecules

to

set

bits

in

a

bit

vector.

The

calculated

fingerprints

were

subsequently

compared

by

the

Tanimoto

similarity

measure

for

bit

strings

The

Tanimoto

coefficient

is

based

on

a

similarity

ratio

and

can

assume

values

between

zero

and

one,

indicating

no

similarity

or

identical

structures

respectively.

It

is

calculated

using

the

bits

of

the

binary

fingerprint

vectors

set

to

one

in

molecule

A

and

molecule

B:.

where

AB

is

the

number

of

bits

set

to

one

in

both

molecules,

A

is

the

number

of

bits

set

to

one

in

molecule

A

and

B

is

the

number

of

bits

set

to

one

in

molecule

B..

Another

method

to

calculate

the

Tanimoto

coefficient

are

the

extended-connectivity

fingerprints

(ECFP)

These

are

used

to

cover

the

calculated

by

OpenBabel

fingerprints,

which

belong

to

the

class

of

radial

fingerprints

and

are

based

on

the

Morgan

algorithm

To

calculate

the

extended-connectivity

fingerprints

the

cheminformatics

toolkit

of

ChemAxon

was

used

(JChem

compr

ChemAxon

-For

comparisons

pre-calculated

conformers

are

superimposed

using

the

Kabsch

algorithm

Based

on

the

normalised

set

of

atoms

in

a

coordinate

system

the

centres

of

mass

for

both

conformers

were

calculated

and

superimposed.

Then

the

principal

axes

of

inertia

are

estimated

and

aligned.

Thereby

the

possible

rotations

are

strongly

reduced

and

only

four

orientations

have

to

be

considered.

For

every

orientation

a

mapping

of

atom

pairs

was

performed

whereupon

atoms

were

fitted

to

each

other

with

the

smallest

possible

distance.

Because

for

atom

pair

assignment

a

maximal

distance

threshold

is

applied,

not

every

atom

is

assigned.

The

rotation

with

the

highest

amount

of

mapped

pairs

was

used

for

further

calculations.

The

normalised

variant

with

the

most

minimal

distance

is

chosen

if

more

than

one

rotation

with

the

same

amount

of

mapped

atom

pairs

exists.

For

this

mapping

a

root-mean-squaredeviation

(rmsd)

was

calculated

and

further

optimised..

Every

molecule's

conformation

was

compared

with

each

conformation

of

the

second

molecule,

resulting

in

up

to

separately

calculated

rmsd

values.

Here,

only

the

smallest

rmsd

value,

i.e.

the

best

superposition

of

the

compounds,

was

stored..

Ligand

Docking

-The

docking

study

was

performed

by

using

LibDock,

a

high-throughput

docking

algorithm

for

library

design

and

library

prioritisation.

This

docking

program

was

provided

by

Accelrys

Discovery

Studio

The

algorithm

positioned

ligands

in

the

protein's

active

site

based

on

polar

and

non-polar

interaction

sites..

cell

lines

-Breast

cancer

cell

lines

were

cultured

in

medium

supplemented

with

inactivated

FBS,

U/ml

penicillin

and

mg/ml

streptomycin.

Cells

were

cultured

at

with

CO

in

a

fully

humidified

atmosphere..

IC

values

of

PARP

inhibitors

-For

the

determination

of

IC

values

of

Vatalanib

and

Compound

we

used

the

HT

universal

colorimetric

PARP

assay

kit

with

histonecoated

strip

wells

(Trevigen,

USA).

Absorbance

was

measured

in

a

Sunrise

microplate

reader

(Tecan,

Switzerland)

at

nm..

foci

analysis

-For

immunofluorescence

microscopic

analyses,

cells

were

grown

on

coverslips.

h

post

treatment

with

(control),

and

μM

Compound

or

Vatalanib,

cells

were

washed

in

PBS,

fixed

in

paraformaldehyde/PBS

min),

permeabilised

with

Triton-X

min)

and

blocked

in

fetal

bovine

serum

for

min

at

room

temperature.

After

incubation

with

anti-phospho-Histone

clone

(mouse

monoclonal

IgG

from

Millipore,

Billerica,

MA,

USA)

overnight

at

cells

were

incubated

with

Alexa

Fluor

chicken

anti-mouse

IgG

secondary

antibody

(Molecular

Probes,

The

Netherlands)

for

h

at

room

temperature

and

counterstained

with

DAPI.

Images

of

foci

and

DAPI-labelled

nuclei

were

acquired

with

a

fluorescent

microscope

Olympus,

Germany)

equipped

with

a

objective

lens

(UPlanFL;

Olympus,

Germany)

and

a

camera

(micropublisher

RTV;

QImaging,

Canada)

with

Openlab

software

(Perkin

Elmer,

Germany).

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O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O B O O O O O O O O O O O O O B O B O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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cts: An R Package for Continuous Time Autoregressive Models via Kalman Filter We describe an R package cts for fitting a modified form of continuous time autoregressive model, which can be particularly useful with unequally sampled time series. The estimation is based on the application of the Kalman filter. The paper provides the methods and algorithms implemented in the package, including parameter estimation, spectral analysis, forecasting, model checking and Kalman smoothing. The package contains R functions which interface underlying Fortran routines. The package is applied to geophysical and medical data for illustration. The discrete time autoregressive model of order p, the AR(p), is a widely used tool for modeling equally spaced time series data. It can be fitted in a straightforward and reliable manner and has the capacity to approximate a second order stationary process to any required degree of precision by choice of a suitably high order. Various information criteria such as the AIC (Akaike 1974) or the BIC (Schwarz 1978) can, in practice be used to determine a suitable order when fitting to a finite data set. This model is clearly not appropriate for irregularly sampled data, for which various authors have advocated the use of the continuous time autoregressive model of order p, the CAR(p). For example Jones (1981) developed an estimation method for the CAR(p) fitted to discretely sampled data through the application of the Kalman filter. However, the CAR(p) model does not share with its discrete counterpart the same capacity to approximate any second order stationary continuous process. Belcher, Hampton, and Tunnicliffe Wilson (1994) modified the parameterization and structure of the CAR(p) model in such a way that this general capacity for approximation was restored, requiring only a relatively minor modification of the estimation procedure of Jones. More recently, see Tunnicliffe Wilson and Morton (2004) , this modified CAR(p) model has been named the CZAR(p) model and expressed in autoregressive form using the generalized continuous time shift operator with the alternative parameterization appearing in a natural manner. Wang, Woodward, and Gray (2009) utilized the methods in Belcher et al. (1994) for fitting time varying nonstationary models. Since the technical details for the Kalman filter on CAR models are scattered in the literature, we give a thorough presentation in this paper, which provides a foundation for the implementations in the R (R Development Core Team 2012) package cts (Tunnicliffe Wilson and Wang 2012) for fitting CAR models with discrete data. The cts package contains typical time series applications including spectral estimation, forecasting, diagnostics, smoothing and signal extraction. The application is focused on unequally spaced data although the techniques can be applied to equally spaced data as well. The paper is organized as follows. Section 2 summarizes the methods from which the cts package was developed. Section 3 outlines the implementations in the package. Section 4 illustrates the capabilities of cts with two data sets. Finally, Section 5 concludes the paper. In this article we have outlined the methods and algorithms for fitting continuous time autoregressive models through the Kalman filter. The theoretical ingredients of Kalman filter have their counterparts in the R package cts, which can be particularly useful with unequally sampled time series data. The cts package utilizes the Fortran program developed by the authors of Belcher et al. (1994) , with substantial additional Fortran and R routines. In this process, two Fortran subroutines in Belcher et al. (1994) have to be substituted since they belong to commercial NAG Fortran Library, developed by the Numerical Algorithms Group. One subroutine was to compute the approximate solution of a set of complex linear equations with multiple right-hand sides, using an Lower-Upper LU factorization with partial pivoting. Another subroutine was to find all roots of a real polynomial equation, using a variant of Laguerre's Method. In the cts package, these subroutines have been replaced by their public available counterparts in the LAPACK & BLAS Fortran Library. All the Fortran programs were written in double precision.. If a constant term is estimated by the default setting ccv="CTES" in the car function, it represents the mean µ, and the model is formulated in terms of (x − µ). In the setting (ccv="MNCT"), the series is not actually mean corrected, but the sample mean is just used to estimate µ in the above model formulation. Several supporting R functions are available in the cts package that extract or calculate useful statistics based on the fitted CAR model, such as model summary, predicted values and model spectrum. In particular, the function car returns objects of class car, for which the following methods are available: print, summary, plot, predict, AIC, tsdiag, spectrum, kalsmo. A detailed description of these functions is available in the online help files. Here a brief introduction will be given and the usage will be illustrated in the next section. Specifying trace=TRUE in car_control could trigger annotated printout of information during the fitting process and major results for the fitted model. The model fitting results can be graphical displayed with plot function. With argument type equal to "spec", "pred" and "diag", respectively, a figure can be plotted for spectrum, predicted values and model diagnostic checking, respectively. Three types of prediction exist: forecast past the end, forecast last L-step, forecast last L-step update. This can be achieved by invoking argument fty=1, 2, 3, respectively. For instance, ctrl=car_control(fty=1, n.ahead=10) can predict 10 steps past the end. Function AIC can generate both t-statistic and AIC values following section 2.3. Function tsdiag follows section 2.4 to provide model diagnostic checking. Indeed, this function provides the backbone for function plot with argument type="diag". Function kalsmo implements the Kalman smoothing described in section 2.5.. The source version of the cts package is freely available from the Comprehensive R Archive Network (http://CRAN.R-project.org). The reader can install the package directly from the R prompt via

O O B O O O O O O O B I O O O B O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O