**Abstract**

The plant kingdom is considered the most important source of medicinal chemicals. *In vitro* culture techniques are being considered a promising alternative to traditional agricultural processes to improve medicinal plants multiplication and their production of pharmaceutical compounds. In this chapter, several *in vitro* culture strategies are discussed to improve secondary metabolites production, including (1) plant kingdom as a source of medicinal chemicals, (2) *in vitro* culture of medicinal plants, (3) culture media optimization, (4) application of suspension cell culture for production of secondary metabolites, (5) elicitation to enhance the productivity of the culture, (6) precursor intermediates feeding, (7) selection of high-yielding cell lines, (8) overexpression of genes that control the production of bioactive compounds, and (9) scale-up production. Also, challenges that hinder the *in vitro* culture of medicinal plants using different techniques and the use of those techniques to produce pharmaceutical compounds are discussed in this chapter, including (a) secondary metabolites toxicity, (b) low growth rate, (c) culture browning, (d) limitation in the application of transformation, (e) somaclonal variation, and (f) vitrification. Therefore, the principal objective of the current chapter was to shed light on the studies on some medicinal plants and the used protocols to overcome some difficulties in terms of *in vitro* propagation that maximize their economic values.

**Keywords:** *In vitro* plant culture, medicinal plants, pharmaceutical compounds, tissue culture, medicinal chemicals


**Author details**

Viviana Mendes Lima1 \*, Sandra M.F. Costa2 and Helena Ribeiro3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Environmental Health, School of Public Health, University of São Paulo, São Paulo, SP, Brazil

2 Laboratory for Studies of Cities, University of Vale do Paraiba, São José dos Campos, SP, Brazil

3 Department of Environmental Health, Faculty of Public Health, University of São Paulo, São Paulo, SP, Brazil


**3.3 DME synthesis**

In 1991, a 10 t/d pilot-scale plant for synthesizing DME from syngas in a slurry bed was developed by Air Products and Chemicals, Inc., and Cu-based methanol synthesis catalysts and methanol dehydration catalysts γ-Al2O3 were adopted. Taiyuan University of Technology, Tsinghua University and Shanxi Coal Chemical Institute of Chinese Academy of Sciences have also done leading research work on slurry bed for DME synthesis. The catalyst for the one-step synthesis of dimethyl ether in the synthesis gas slurry bed is similar to the one-step catalyst in the fixed bed, which includes a methanol synthesis catalyst and a methanol dehydration catalyst. The methanol synthesis catalyst generally adopts CuZnAl catalyst or modified CuZnAl catalyst, and the catalyst for methanol dehydration generally adopts γ-Al2O3, zeolite, etc. **Table 2** summarizes the progress of DME synthesis in a slurry-bed reactor [24]. It can be seen that JFE (formally NKK) Corporation produced DME while Air Products Chemicals produced DME and methanol.


**11. Effects on the cholinergic system**

Acetylcholine is a neurotransmitter and modulator that, when bound to nicotinic receptors, favors neuronal excitability, and when bound to muscarinic receptors (mainly of the M2 subtype), inhibits the inhibitory activity triggered by the activation of GABAA receptors, i.e., disinhibits the postsynaptic terminal [31]. Two cholinergic pathways are particularly important in the pathophysiology of hepatic encephalopathy (**Figure 6**): (1) those originating from the ascending activating reticular system in the brainstem (particularly the laterodorsal tegmental nuclei and pedunculopontine nuclei) and (2) those originating from the basal forebrain, an area that includes the nucleus basalis of Meynert, the medial septal nucleus, and the diagonal band of Broca [37].

The projections of acetylcholine that originate in the ascending reticular activating system extend to the prefrontal cortex, basal forebrain, thalamus, hypothalamus, amygdala, and hippocampus; they are considered to be involved in vigilance (sustained attention) [39]. Cholinergic neurons that originate in the basal forebrain extend to the prefrontal cortex, hippocampus, and amygdala; they are involved with the formation of episodic memory [37].

The effects of acetylcholine are terminated by two enzymes, acetylcholinesterase and butyrylcholinesterase. Both convert acetylcholine to choline, which is then transported back to the presynaptic terminal for further synthesis of this neurotransmitter [37]. Cirrhosis is associated with an increase of approximately 30% in acetylcholinesterase activity in humans, which contributes to a decrease in acetylcholine levels and a consequent potentiation of the effects of GABAergic tonus [34]. Little is known about how chronic hyperammonemia and neuroinflammation induce changes in the cholinergic system [31]. There is no correlation, for example, between serum ammonia levels and acetylcholinesterase activity [34]. However, experimental studies have shown that the increased availability of acetylcholine in

#### **Figure 6.**

*The cholinergic pathways originate in the basal forebrain (BF) and reticular formation (RF). They extend to the prefrontal cortex and medial portion of the temporal cortex. AN: accumbens nucleus, GP: globus pallidus, S: striatum, and T: thalamus.*

**51**

**Figure 7.**

*The Neurobiology of Hepatic Encephalopathy DOI: http://dx.doi.org/10.5772/intechopen.86320*

severity of hepatic encephalopathy [31].

**12. Effects on the serotonergic system**

**13. Effects on the histaminergic system**

*cord. AN: accumbens nucleus, GP: globus pallidus, S: striatum, and T: thalamus.*

the synaptic cleft, either by direct administration or by inhibition of its degradation, is related to the reduction in glutamate neurotoxicity and improvement in the

Serotonin is a neurotransmitter and modulator that favors the excitability of cortical neurons; a decrease in serotonergic tonus potentiates the effects of increased GABAergic tone [31]. Serotonergic neurons have both ascending and descending projections (**Figure 7**). The ascending projections originate in the raphe nuclei in the brainstem and extend to the cerebellum, hypothalamus, thalamus, amygdala, hippocampus, striatum, accumbens nucleus, basal forebrain, and prefrontal cortex [37]. They are related to the regulation of mood, hunger, impulsivity, and circadian rhythm [35]. The descending projections extend to the lower portions of the

The dysfunction of the serotonergic system has been widely documented in both minimal hepatic encephalopathy and overt hepatic encephalopathy: it underlies several early neuropsychiatric disorders in the disease, such as mood and sleep disorders. Serotonin levels correlate with the severity of cirrhosis and the degree of portosystemic shunt [35]. There is an increase in the circulation of l-tryptophan, the precursor amino acid of this neurotransmitter, in blood and cerebrospinal fluid. It is hypothesized that hyperammonemia not only stimulates serotonin synthesis, but also its degradation by the enzyme monoamine oxidase A (MAO-A), which is shown by the concomitant increase of the main product of its metabolism, 5-hydroxyindoleacetic acid [31, 33, 35].

Histamine acts in conjunction with serotonin to regulate the circadian rhythm [31]. Histaminergic neurons originate in the tuberomammillary nucleus of the hypothalamus and make extensive projection throughout the central nervous system, including the spinal cord (**Figure 8**) [37]. Significant increase in histamine

*The ascending serotonergic pathway originates in the raphe nucleus (RN) and extends to the medial portion of the temporal cortex and prefrontal cortex, while the descending pathway modulates the activity of the spinal*

brainstem and spinal cord, being important for pain regulation [37].

*Liver Disease and Surgery*

**11. Effects on the cholinergic system**

with the formation of episodic memory [37].

**50**

**Figure 6.**

*S: striatum, and T: thalamus.*

*The cholinergic pathways originate in the basal forebrain (BF) and reticular formation (RF). They extend to the prefrontal cortex and medial portion of the temporal cortex. AN: accumbens nucleus, GP: globus pallidus,*

that the activation of glutamatergic metabotropic receptors in the substantia nigra can also cause a decrease in the locomotion of rodents, since the substantia nigra has a second pathway of GABAergic neurons that extends into the thalamus, where a group

Acetylcholine is a neurotransmitter and modulator that, when bound to nicotinic

The projections of acetylcholine that originate in the ascending reticular activating system extend to the prefrontal cortex, basal forebrain, thalamus, hypothalamus, amygdala, and hippocampus; they are considered to be involved in vigilance (sustained attention) [39]. Cholinergic neurons that originate in the basal forebrain extend to the prefrontal cortex, hippocampus, and amygdala; they are involved

The effects of acetylcholine are terminated by two enzymes, acetylcholinesterase

and butyrylcholinesterase. Both convert acetylcholine to choline, which is then transported back to the presynaptic terminal for further synthesis of this neurotransmitter [37]. Cirrhosis is associated with an increase of approximately 30% in acetylcholinesterase activity in humans, which contributes to a decrease in acetylcholine levels and a consequent potentiation of the effects of GABAergic tonus [34]. Little is known about how chronic hyperammonemia and neuroinflammation induce changes in the cholinergic system [31]. There is no correlation, for example, between serum ammonia levels and acetylcholinesterase activity [34]. However, experimental studies have shown that the increased availability of acetylcholine in

of GABAergic interneurons inhibit motor cells, resulting in hypokinesia [26].

receptors, favors neuronal excitability, and when bound to muscarinic receptors (mainly of the M2 subtype), inhibits the inhibitory activity triggered by the activation of GABAA receptors, i.e., disinhibits the postsynaptic terminal [31]. Two cholinergic pathways are particularly important in the pathophysiology of hepatic encephalopathy (**Figure 6**): (1) those originating from the ascending activating reticular system in the brainstem (particularly the laterodorsal tegmental nuclei and pedunculopontine nuclei) and (2) those originating from the basal forebrain, an area that includes the nucleus basalis of Meynert, the medial septal nucleus, and the diagonal band of Broca [37].

the synaptic cleft, either by direct administration or by inhibition of its degradation, is related to the reduction in glutamate neurotoxicity and improvement in the severity of hepatic encephalopathy [31].


*Article* **Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem**

**Yule Wang and Wanliang Wang \***

> College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantuminspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance the diversity and convergence performance and improve the performance in high-dimensional cases for 0-1 knapsack problems. The proposed algorithm adopts quantum computing principles such as quantum superposition states and quantum gates. It also uses adaptive mutation operations of differential evolution, crossover operations of differential evolution, and quantum observation to generate new solutions as trial individuals. Selection operations are used to determine the better solutions between the stored individuals and the trial individuals created by mutation and crossover operations. In the event that the trial individuals are worse than the current individuals, the adaptive grey wolf optimizer and quantum rotation gate are used to preserve the diversity of the population as well as speed up the search for the global optimal solution. The experimental results for 0-1 knapsack problems confirm the advantages of QDGWO with the effectiveness and global search capability for knapsack problems, especially for high-dimensional situations.

**Keywords:** quantum computing; differential evolution; grey wolf optimizer; evolutionary algorithm; 0-1 knapsack problem


*5.3.2 Unwashed PCM: different magnifications (100X, 500X, and 3000X)*

In **Figure 13**, image A at magnification 100X shows the web linking oriented fibres of the non-woven PCM sample. Image B at magnification 500X there are 2 different form of PCMs observed, the PCM microcapsules in the web and the mPCM wrapped around the fibre. Image C at magnification 3000X clearly illustrates the PCM capsules within web.

**Figure 12.**

*SEM images of unwashed sample at different areas.*

**Figure 13.** *SEM images of unwashed PCM at different magnifications.*

*The Effect of Laundering on the Physical and Thermal Properties of Phase Change Textile… DOI: http://dx.doi.org/10.5772/intechopen.105064*


**1. Introduction**

The literature [1] indicates a strong relationship between the rising of soot concentration in the environment and the increasing intensity of global warming over the past 20 years. Greenhouse effect and global warming are the most pressing issues the world is facing in 2019. The noticeable problem is the are average 0.17°C per year global warming since pre-industrial levels (1970–2019), also the 0.09°C warming and increasing pH of oceans 1950s, 3.2 cm sea-levels growth per decade, a large number of extreme heatwaves in last decade [2, 3]. Unless the in 2019 we act to reduce the causes of global warming, we unite and commit, and pledge that the changes to mitigate global warming are fully implemented, the adverse effects of climate change will go on.

The natural phenomenon of the greenhouse effect plays a vital role in climate change for decades. The burning of fossil fuels has contributed intensely to the natural greenhouse effect. This aggregated greenhouse effect stems from a rise in the atmospheric concentrations termed greenhouse gases. Greenhouse gases in the atmosphere lead to climate change. Soot, along with NOx, CO, and CO2 are considered significant greenhouse gases and pollutants.

In recent years after the industrial revolution, diesel engines are used extensively in multiple areas for their fewer CO2 emissions, better fuel efficiency, and improved economy [4]. Soot or unburnt hydrocarbons (UHC) present in the exhaust of internal combustion diesel engines. To be in accord with the intense emission levels imposed by the law, destruction of above mentioned delirious compounds is the necessity of the era. Their conversion into a nonparticulate form before these toxic substances released into the surrounding environment is also another alternative. Soot has been counted in the chief scale of severity among such classification. For this purpose, it is proposed, the of a mixture of catalytically active components coated with perovskites will cause the parallel conversion of UHC into subsequent oxides while could be absorbed in the exhaust filters and by the environment.

While soot continues to harm human race in metropolitan cities its effect is unseen to healthy eyes, when carbon black enters your bloodstream, it can cause a wide array of severe health issues, including respiratory problems, infections, shortness of breath, bronchitis, asthma, stroke, heart attack, cancer, and premature death. Soot not only causes smog that decreases visual clarity but stays in the atmosphere longer than carbon dioxide. Carbon dioxide can be absorbed by trees, but soot cannot be 'fixed' by natural means. Soot causes global warming by absorbing sunlight and directly heat the surrounding air. **Figure 1** clearly

**81**

*Reducing Green House Effect Caused by Soot via Oxidation Using Modified LaFe1-xCuxO3…*

demonstrates how particulate matter is contributing to global warming as a significant global pollutant. **Figure 2** zones in the culmination of sizes of PM towards soot indicating inversely proportional relationship between the size of

This particulate matter (pm) in the form of soot released from the diesel combustion process has grasped plenty of attention. The reason for that is it can give rise to serious environmental and health problems [7–9]. Particulate matter is filtered with diesel particulate filters (DPF) in the current scenario [10] which captures soot and requires regeneration to maintain performance. To tackle this issue, researchers have conducted some works about regeneration, but none led to satisfaction. Methods involving in oxidation of particulate matter directly are researched upon. But they all need a high temperature above 600°C which might

Therefore oxidation of soot is important to resolve environmental issues in many parts of the world. However, soot oxidation is not a process that can occur under

Csoot + O2 → CO2 + H2O

2CO + O2 → 2CO2

Csoot + NO → ½ N2 + CO2 + H2O

As seen soot oxidation mainly involves the reaction of formation of CO2, so does the enthalpy of reaction is enthalpy of formation. Enthalpy at 298.15 K for carbon dioxide formation is 393.5 kJ/mol and the formation of carbon monoxide is 110.53 kJ/mol. Soot oxidation is a slow process at high temperatures with relatively

Activation energy of above reaction at various temperature is found as follows:

770–1250 K:143.5 kJ/mol

800–1000 K:161.2 kJ/mol—Accurate and depends on the partial pressure of O2

1100–1400 K:164 kJ/mol

natural conditions, and so further analysis is needed. Following reactions take place for soot oxidation:

*DOI: http://dx.doi.org/10.5772/intechopen.90460*

*Size of soot as the comparison with other particulate matter.*

PM and its banality [5, 6].

**Figure 2.**

destroy DPF [11].

high activation energy.

(root pO2)—(7)

**Figure 1.**

*Pollution caused by particulate matter.*

*Reducing Green House Effect Caused by Soot via Oxidation Using Modified LaFe1-xCuxO3… DOI: http://dx.doi.org/10.5772/intechopen.90460*

**Figure 2.**

*Global Warming and Climate Change*

The literature [1] indicates a strong relationship between the rising of soot concentration in the environment and the increasing intensity of global warming over the past 20 years. Greenhouse effect and global warming are the most pressing issues the world is facing in 2019. The noticeable problem is the are average 0.17°C per year global warming since pre-industrial levels (1970–2019), also the 0.09°C warming and increasing pH of oceans 1950s, 3.2 cm sea-levels growth per decade, a large number of extreme heatwaves in last decade [2, 3]. Unless the in 2019 we act to reduce the causes of global warming, we unite and commit, and pledge that the changes to mitigate global warming are fully implemented, the adverse effects of

The natural phenomenon of the greenhouse effect plays a vital role in climate change for decades. The burning of fossil fuels has contributed intensely to the natural greenhouse effect. This aggregated greenhouse effect stems from a rise in the atmospheric concentrations termed greenhouse gases. Greenhouse gases in the atmosphere lead to climate change. Soot, along with NOx, CO, and CO2 are consid-

In recent years after the industrial revolution, diesel engines are used extensively in multiple areas for their fewer CO2 emissions, better fuel efficiency, and improved economy [4]. Soot or unburnt hydrocarbons (UHC) present in the exhaust of internal combustion diesel engines. To be in accord with the intense emission levels imposed by the law, destruction of above mentioned delirious compounds is the necessity of the era. Their conversion into a nonparticulate form before these toxic substances released into the surrounding environment is also another alternative. Soot has been counted in the chief scale of severity among such classification. For this purpose, it is proposed, the of a mixture of catalytically active components coated with perovskites will cause the parallel conversion of UHC into subsequent oxides while could be absorbed in the exhaust filters and by the environment. While soot continues to harm human race in metropolitan cities its effect is unseen to healthy eyes, when carbon black enters your bloodstream, it can cause a wide array of severe health issues, including respiratory problems, infections, shortness of breath, bronchitis, asthma, stroke, heart attack, cancer, and premature death. Soot not only causes smog that decreases visual clarity but stays in the atmosphere longer than carbon dioxide. Carbon dioxide can be absorbed by trees, but soot cannot be 'fixed' by natural means. Soot causes global warming by absorbing sunlight and directly heat the surrounding air. **Figure 1** clearly

**1. Introduction**

climate change will go on.

ered significant greenhouse gases and pollutants.

**80**

**Figure 1.**

*Pollution caused by particulate matter.*

*Size of soot as the comparison with other particulate matter.*

demonstrates how particulate matter is contributing to global warming as a significant global pollutant. **Figure 2** zones in the culmination of sizes of PM towards soot indicating inversely proportional relationship between the size of PM and its banality [5, 6].

This particulate matter (pm) in the form of soot released from the diesel combustion process has grasped plenty of attention. The reason for that is it can give rise to serious environmental and health problems [7–9]. Particulate matter is filtered with diesel particulate filters (DPF) in the current scenario [10] which captures soot and requires regeneration to maintain performance. To tackle this issue, researchers have conducted some works about regeneration, but none led to satisfaction. Methods involving in oxidation of particulate matter directly are researched upon. But they all need a high temperature above 600°C which might destroy DPF [11].

Therefore oxidation of soot is important to resolve environmental issues in many parts of the world. However, soot oxidation is not a process that can occur under natural conditions, and so further analysis is needed.

Following reactions take place for soot oxidation:

Csoot + O2 → CO2 + H2O 2CO + O2 → 2CO2 Csoot + NO → ½ N2 + CO2 + H2O

As seen soot oxidation mainly involves the reaction of formation of CO2, so does the enthalpy of reaction is enthalpy of formation. Enthalpy at 298.15 K for carbon dioxide formation is 393.5 kJ/mol and the formation of carbon monoxide is 110.53 kJ/mol. Soot oxidation is a slow process at high temperatures with relatively high activation energy.

Activation energy of above reaction at various temperature is found as follows:


**2.2.4 PIM to PSM step: Formalization of ontology conceptual model**

It can be seen that the high-level ontology conceptual model described by UML is independent of a specific ontology language. So, in order to generate the formal file encoded by a specific ontology language, we need to transform the PIM into PSM according to the concrete model transformation rules. Figure 15 shows the general model transformation mechanism of model driven architecture. Model transformation is essentially to map the source model elements to other elements of the target model. Models are usually the instantiation of its meta-model. The model transformation rules are generally defined in the metamodel level and then model transformation engine apply these rules to the model level to complete the model transformation.

Fig. 15. The principle of model transformation.

Therefore, in order to transform the high-level ontology conceptual model (i.e. PIM) into platform specific model (i.e. PSM), we need to define the transformation rules according to the source and target metamodels. In our proposed approach, the high-level ontology conceptual model (i.e. PIM) is modeled by UML2.0, and the source metamodel is UML2.0 metamodel obviously. So we need a target metamodel relating to specific ontology language to describe the formal ontology model (i.e. PSM). In fact, OMG (Object Management Organization), which is the promoter of MDA, has considered this problem. In May 2009, OMG released the Ontology Definition Metamodel (ODM) v1.0 (OMG, 2009) based on the

Telecommunications Service Domain Ontology:

owl:ObjectPropert Class

owl:DatatypeProperty Class

Table 2. A part of UML Profile for RDF and OWL.

is UML Profile for RDF and OWL in this proposed approach.

OWL vocabularies.

Semantic Interoperation Foundation of Intelligent Integrated Services 199

Therefore, in this approach, the metamodel of PSM employs the UML Profile for RDF and OWL defined in ODM specification. This profile is designed to support modelers developing vocabularies in Resource Description Framework (RDF) (W3C, 2004b) and richer ontologies in the Web Ontology Language (OWL) through reuse of UML notation using tools that support UML2 extension mechanisms. Table 2 specifies a part of stereotypes set that comprise the UML2 Profile for using UML to represent RDF/S and

**RDF**、**RDFS and OWL ontology UML Base Class UML Stereotype**  rdfs:Resource Class 《rdfsResource》 rdfs:Datatype Class 《rdfsDatatype》 rdfs:domain Association 《rdfsDomain》 rdfs:range Association 《rdfsRange》 rdfs:subClassOf Generalization 《rdfsSubClassOf》 rdfs:subPropertyOf Generalization 《rdfsSubPropertyOf》 owl:Class Class 《owlClass》 owl:Restriction Class 《owlRestriction》

> AssociationClass Property Association

> AssociationClass Property Association

owl:equivalentClass Constraint 《equivalentClass》 owl:disjointWith Constraint 《disjointWith》

After the source and target metamodels are determined, we can define the model transformation rules from high-level ontology conceptual model (i.e. PIM) to ontology language related model (i.e. PSM). For example, based on the Table 1 and Table 2, we can define the following model transformation rules to support the model transformation like Figure 17. Notably, the source metamodel is UML2.0 metamodel and the target metamodel

When the transformation rules are defined, the model transformation engine can scan the elements of source model and then transform them into the corresponding elements of target model according to the transformation rules. As model transformation is a key technique used in model-driven architecture. In 2002, OMG issued a Request for proposal (RFP) on MOF Query/View/Transformation to seek a standard compatible with the MDA recommendation suite (UML, MOF, OCL, etc.). Several replies were given by a number of companies and research institutions that evolved during three years to produce a common proposal that was submitted and approved. QVT (Query/View/Transformation) (OMG,

《objectProperty》

《datatypeProperty》

meta-modeling mechanism of MDA. This specification represents the foundation for an extremely important set of enabling capabilities for MDA based software engineering, namely the formal grounding for representation, management, interoperability, and application of business semantics. The ODM is applicable to knowledge representation, conceptual modeling, formal taxonomy development and ontology definition, and enables the use of a variety of enterprise models as starting points for ontology development. ODM is based on the Meta Object Facility (MOF) (OMG, 2006) meta-modeling architecture of MDA, illustrated by Fig.16, which is based on the traditional four layer metadata architecture. From top to bottom, meta-data is abstracted to 4 layers: M3 (meta-meta model), M2 (meta model), M1 (model) and M0 (object and instance). The under-layer is the instance of its up-layer in turn. M3 layer is the end of meta-layer, namely, MOF is self-described. MOF is a common, abstract language used to define meta-model. It defines some metamodeling constructs, such as Class, DataType, Association, Package, and Constraint. So the meta-model of ODM or UML can be defined by MOF, whose power just lies in its capability to enable interoperability among different meta-models. Currently, there are 2 approaches to construct meta-models in M2 layer. One is to make use of MOF to define a completely new meta-model from syntax to semantics. Although this approach supports to define a new meta-model that will perfectly match the concepts and relation of the concrete domain, this need the underlying programming realization of corresponding new modeling tool. This is heavy-weight meta-modeling, such as UML and ODM. The other is to extend the existent UML meta-model and then construct a standard UML Profile through UML extension mechanism (Stereotype, TaggedValue, Constraints). This approach allows both defining domain specific conception and relation through UML extension mechanism and using the intrinsic UML elements. So it's a light-weight meta-modeling approach and most of existent MDA tools support this UML Profiling-based meta-modeling mechanism currently. There is no need to develop a new modeling tool. From the above analysis, the UML Profiling-based meta-modeling mechanism approach is adopted in our approach.

Fig. 16. ODM: the integration of semantic web and model driven architecture.

meta-modeling mechanism of MDA. This specification represents the foundation for an extremely important set of enabling capabilities for MDA based software engineering, namely the formal grounding for representation, management, interoperability, and application of business semantics. The ODM is applicable to knowledge representation, conceptual modeling, formal taxonomy development and ontology definition, and enables the use of a variety of enterprise models as starting points for ontology development. ODM is based on the Meta Object Facility (MOF) (OMG, 2006) meta-modeling architecture of MDA, illustrated by Fig.16, which is based on the traditional four layer metadata architecture. From top to bottom, meta-data is abstracted to 4 layers: M3 (meta-meta model), M2 (meta model), M1 (model) and M0 (object and instance). The under-layer is the instance of its up-layer in turn. M3 layer is the end of meta-layer, namely, MOF is self-described. MOF is a common, abstract language used to define meta-model. It defines some metamodeling constructs, such as Class, DataType, Association, Package, and Constraint. So the meta-model of ODM or UML can be defined by MOF, whose power just lies in its capability to enable interoperability among different meta-models. Currently, there are 2 approaches to construct meta-models in M2 layer. One is to make use of MOF to define a completely new meta-model from syntax to semantics. Although this approach supports to define a new meta-model that will perfectly match the concepts and relation of the concrete domain, this need the underlying programming realization of corresponding new modeling tool. This is heavy-weight meta-modeling, such as UML and ODM. The other is to extend the existent UML meta-model and then construct a standard UML Profile through UML extension mechanism (Stereotype, TaggedValue, Constraints). This approach allows both defining domain specific conception and relation through UML extension mechanism and using the intrinsic UML elements. So it's a light-weight meta-modeling approach and most of existent MDA tools support this UML Profiling-based meta-modeling mechanism currently. There is no need to develop a new modeling tool. From the above analysis, the UML Profiling-based meta-modeling mechanism approach is adopted in our approach.

Fig. 16. ODM: the integration of semantic web and model driven architecture.

Therefore, in this approach, the metamodel of PSM employs the UML Profile for RDF and OWL defined in ODM specification. This profile is designed to support modelers developing vocabularies in Resource Description Framework (RDF) (W3C, 2004b) and richer ontologies in the Web Ontology Language (OWL) through reuse of UML notation using tools that support UML2 extension mechanisms. Table 2 specifies a part of stereotypes set that comprise the UML2 Profile for using UML to represent RDF/S and OWL vocabularies.

Table 2. A part of UML Profile for RDF and OWL.

After the source and target metamodels are determined, we can define the model transformation rules from high-level ontology conceptual model (i.e. PIM) to ontology language related model (i.e. PSM). For example, based on the Table 1 and Table 2, we can define the following model transformation rules to support the model transformation like Figure 17. Notably, the source metamodel is UML2.0 metamodel and the target metamodel is UML Profile for RDF and OWL in this proposed approach.

When the transformation rules are defined, the model transformation engine can scan the elements of source model and then transform them into the corresponding elements of target model according to the transformation rules. As model transformation is a key technique used in model-driven architecture. In 2002, OMG issued a Request for proposal (RFP) on MOF Query/View/Transformation to seek a standard compatible with the MDA recommendation suite (UML, MOF, OCL, etc.). Several replies were given by a number of companies and research institutions that evolved during three years to produce a common proposal that was submitted and approved. QVT (Query/View/Transformation) (OMG,

Telecommunications Service Domain Ontology:

**<IP_ADDRESS> Model to code transformation theory**

*Definition 2. Relation Matrix (RM)*

1 2

*Definition 3. Connected subgraph*

11 12 1 21 22 2

*aa a aa a*

*n n nn*

*aa a*

... ... ... ... ... ... ...

*n n*

Gn}, these subgraphs meet the following conditions:

*Definition 4. Model Transformation Automaton (MTA)*

class node. ∀ q∈Q, q is called a state of MTA.

*aij* ( *aij* ≠ 0 ), δ:Q×Σ→Q in relation matrix.

• q0: The begin state of MTA, q0∈Q.

and Gj at the same time. That is, ¬∃ ∈ ∩ ∈ *x x Gi x Gj* , .

and y (without regard to the direction of the edge).

*Definition 1. Ontology model triples: UmlOnt (C, R, G).*

technology.

firstly.

looks like

of relation node.

node.

Semantic Interoperation Foundation of Intelligent Integrated Services 201

In order to generate the formal ontology file encoded by OWL, the PSM based on UML Profile for RDFS and OWL should be transformed into ontology file encoded by OWL, which is a model to code transformation process, which involves the model scanning

Before we introduce the concrete transformation process, some related definitions are given

PSM based on the UML class diagram can be represented by a triple: UmlOnt(C, R, G). C is the class node set, and it is an ontology class definition of the concept in PSM. R is the relation node set, and it is the definition of the relations among ontology class. G is the relation set of C and R, which describes the relations among the nodes in C and R set.

Relation matrix is a n order square matrix, including elements *aij* in total of n \* n, which

describe the relations between the class nodes and relation nodes in the PSM. And *aij* indicates whether there is relation between class node *i* and class node *j,* as well as the type

Given a directed graph, which can be divided to several connected subgraphs {G1, G2, …,

• In any two connected subgraphs Gi and Gj, there is no such a node x which is both in Gi

• In a connected subgraph Gi, there always exist directed edge between any two nodes x

• Q: A nonempty and finite set of states and one state in it corresponds to an ontology

• Σ: Input events table, in which one input event corresponds to an ontology relation

• δ: Transfer function. One transfer function corresponds to a nonzero number

Model transformation automaton is a quintuple: MTA = (Q, Σ, δ, q0, F), including:

, denoted as A = ( ) *aij nn* ( i,j = 1, 2, 3, ……, n ) is used to

**2.2.5 PSM to code step: Formalization of ontology conceptual model**

2008) is a standard set of languages for model transformation defined by the Object Management Group. Currently, some MDA tools have declared to support the complete or part functions of QVT. For example, by using the transformation rules, the source model in Figure 14 is transformed into a target model in Figure 18.

Fig. 17. A part of PIM to PSM transformation rules definitions.

Fig. 18. PSM: A part of ontology specific model based on UML Profile for RDF and OWL.


*3.2. Study Characteristics*

The specific characteristics of the studies included in the MA are summarized in Table 1. The control arm in all studies was platinum-based chemotherapy with pemetrexed (three studies [17,20,21,28]) or with nab-paclitaxel/paclitaxel (five studies [21–25,29]). In one three-arm trial (IMpower150 [24]), bevacizumab was added in the control and experimental arm. This study included two experimental arms: carboplatin, paclitaxel, and atezolizumab (arm A) and carboplatin, paclitaxel, bevacizumab, and atezolizumab (arm B) versus carboplatin, paclitaxel, and bevacizumab (arm C). No comparisons between arms A and C were performed because the HR may not reflect the actual effect of add-on immunotherapy (atezolizumab plus chemotherapy vs. bevacizumab plus chemotherapy).

**Figure 1.** Flow chart of study selection (up to 1 January 2020). NSCLC, non-small-cell lung cancer; RCTs, randomized controlled trials.

\* Patients with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations excluded. PD-L1, programmed cell death-ligand 1; PFS, progression-free survival; OS, overall survival; ITT, intention-to-treat; WT, wildtype; +, plus (combination therapy).

Three studies tested anti-PD1 antibodies [17,21,22,28], and four studies tested anti-PD-L1 antibodies [20,23–25,29]. Patients with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations were included in two clinical trials assessing atezolizumab [24,25]. Regarding the histology, four studies included patients with nonsquamous NSCLC [20,21,24,25], two included patients with squamous NSCLC [22,29], and one evaluated patients presenting both histological types [21] (note that the primary endpoint in CheckMate-227 part 2 was OS in nonsquamous mNSCLC patients only; however, both histological types were considered for this MA).

An all-comers design was used in all the studies, with NSCLC patients entering the trial regardless of their PD-L1 expression status. Stratification was performed based on this biomarker in all trials. Thus, subjects were classified as PD-L1-negative or PD-L1-positive, and within this group, investigators distinguished patients with high or low expression levels [20,25,29]. In the atezolizumab trials [20,23–25,29], levels were considered high (TC3 or IC3) when PD-L1 expression was recorded on at least 50% of tumor cells or at least 10% of tumor-infiltrating immune cells (TIICs) by immunohistochemistry; levels were considered low–intermediate, or TC1/2 or IC1/2, when expression was reported on at least 1% of tumor cells or TIICs and less than 50% of tumor cells or less than 10% of TIICs by immunohistochemistry; and PD-L1-negative status, or TC0 and IC0, was determined when expression was reported on less than 1% of tumor cells and TIICs. Similar criteria were followed in trials assessing pembrolizumab or nivolumab, but PD-L1 expression was only measured in tumor cells [17,21,22,28].

According to the eligibility criteria, none of the studies included patients who had received prior treatment for metastatic disease. However, in terms of therapy for nonmetastatic disease, although most of the studies included treatment-naïve patients, in those evaluating pembrolizumab (KEYNOTE-189 [17,28] and KEYNOTE-407 [22]) or atezolizumab [20,23–25,29], subjects had received previous therapies (Supplementary Table S1).

Coprimary endpoints for six clinical trials were PFS and OS [17,20,22–25,28,29]. The primary endpoint for CheckMate-227 part 2 was OS in nonsquamous NSCLC patients; PFS was assessed as a secondary endpoint [21]. Mature PFS data were reported in all the studies included in this MA [17,20–25,28,29], while final data for OS were available for only one of them [21]. Interim analyses were provided for the other six studies [17,20,22–25,28,29]. Both endpoints were evaluated in the intention-to-treat (ITT) population and specifically in the wildtype population (without EGFR or ALK mutations) in the IMpower130 [25] and IMpower150 studies [23,24] (see Supplementary Table S2 for the available information on patients with mutations in IMpower150). IMpower150 was the only study in which a subsequent subgroup analysis in patients with EGFR mutations or baseline liver metastasis was performed [23]. Additionally, one or both coprimary endpoints were analyzed according to different subgroups in all the studies included in the MA (PFS in six clinical trials [17,22–25,28,29] and OS in five studies [17,21,22,25,28,29]).

Patient population characteristics of all the studies included in the MA are shown in Supplementary Table S3.

#### *3.3. E*ffi*cacy Endpoints in the Overall Population*

Median PFS ranged from 4.8 to 6.8 months in the control arms and from 6.3 to 8.8 months in the treatment arms. Median OS ranged from 10.7 to 14.7 months in the control arms and from 14.2 to 22.0 months in the treatment arms. MA results demonstrated that the addition of a PD-(L)1 to chemotherapy was associated with improved PFS (PFS: HRpooled = 0.61, 95% CI: 0.57–0.65, *p* < 0.001, Figure 2A) and OS (OS: HRpooled = 0.76, 95% CI: 0.67–0.86; *p* < 0.001, Figure 2B) compared with chemotherapy alone. The objective response rate (ORR) was also significantly improved with the PD-(L)1 inhibitor–chemotherapy combination (odds ratio (ORpooled) = 2.12, 95% CI: 1.70–2.63, *p* < 0.001, Supplementary Figure S1). The best ORR values were obtained in the IMpower150 (ORR = 56.4%) trial for nonsquamous NSCLC and KEYNOTE-407 for squamous NSCLC (57.9%). Notably, in terms of both OS and ORR, there was significant heterogeneity across the six trials (I2 = 52.07%, *p* = 0.03; I2 = 67.42%, *p* = 0.005).


Bone represents a complex, dynamic microenvironment and establishment of metastatic lesions in the skeleton requires tumor cell interactions with a number of cell types in the marrow. To examine the suitability of our cell culture system to study multi-cell interactions, we incorporated BMMs into 3D tumor spheroids growing under control and Transwell conditions (**Figure 4A**). Pre-labeling of PC3 tumor cells with CTO allowed visualization of the tumor growing in 3D culture (**Figures 4B,C**). We tracked the incorporation of BMMs into the tumor spheroid by F4/80 immunofluorescence staining. Tumors growing under Transwell conditions with adipocytes attracted more macrophages than those cultured under control conditions (**Figures 4B,C**; green fluorescence). Incorporation of BMMs into the spheroid was also visible in the optical slices taken in the middle of the spheroid (**Figures 4D–G**). These observations suggest that adipocytes might be inducing phenotypic changes in the tumor cells and/or macrophages to promote the BMM affinity for the tumor. Further studies are needed to confirm this hypothesis.

## Imaging Live Tumor Cell-Driven Proteolysis in the Presence of Marrow Adipocytes

To grow and thrive in their surrounding environments, tumors have to interact with the extracellular matrix (ECM) via biochemical and mechanical processes. Proteolysis of the ECM is one of the key aspects of tumor invasion (52, 53), and we have previously shown that exposure to adipocyte-derived factors increases invasive potential of prostate carcinoma cells (5). One way to effectively visualize ECM proteolysis by the tumor cells and other cell types residing in the tumor microenvironment is through a confocal microscopy assay utilizing quenchedfluorescent (DQ) proteins, an approach developed by the Sloane laboratory over a decade ago (53). We have previously utilized this technique to demonstrate that prostate carcinoma cells are capable of degrading quenched fluorescent derivatives of collagen IV (basement membrane) and collagen I (organic matrix of the bone) (33). Therefore, we used this approach to determine whether exposure to adipocyte-derived factors affects the collagen IV degradation in our model. PC3 cells plated on rBM containing DQ-IV were allowed to establish into spheroids by growing for 3 days under control or Transwell conditions before adding CTO-labeled BMMs and allowing cultures to continue for additional 2 days (**Figure 5A**). Degradation of the DQ-collagen IV, observed as green fluorescence, was captured and quantified at the end of 5-day culture. In agreement with our previous findings (33), fluorescent cleavage products of DQ-collagen IV were found both intracellularly and pericellularly (**Figures 5B–E**). Similar levels of fluorescence were detected in PC3 cells cultured alone (without BMMs) regardless if they were under control or Transwell conditions (**Figures 5B,D,F,H,J**), suggesting no measurable contribution of adipocytes to tumor-driven proteolysis.

Since macrophages are important sources of proteases and key contributors to tumor-associated proteolysis (34, 53), we also measured and quantified the DQ-IV fluorescence following the 2-day coculture of pre-established tumor spheroids exposed to macrophages (**Figures 5C,E,G,I,J**). Surprisingly, despite the significant infiltration of BMMs into tumor spheroids exposed to adipocytes in a Transwell coculture and visible BMM-associated proteolysis (**Figures 5E,I**), there was no significant increase in overall tumor-associated DQ-IV degradation compared with cultures grown without macrophages (**Figure 5J**).

Interestingly, when macrophages were incorporated into the culture at the time of seeding the tumor cells (**Figure 6A**), significant changes were observed in the morphology of the resulting spheroids and in the tumor-associated proteolysis. Specifically, under control conditions, addition of BMMs led to formation of less compacted and more disorganized tumor spheroids compared with tumor cells cultured alone (**Figures 6B,F** vs. **Figures 6C,G**). This change in morphology was also associated with a significant increase in DQ-IV proteolysis (**Figure 6J**). Even more pronounced effects of BMMs were observed in cultures exposed to adipocytes in the Transwell system (**Figure 6D,H** vs. **Figures 6E,I**). Formation of highly disorganized tumor cell/ BMM clusters with high levels of DQ-IV fluorescence (**Figure 6J**) suggested potentially important contribution of adipocytes to tumor- and BMM-driven proteolytic activity in this model.

## Quantifying Tumor Cell Invasion Toward Adipocytes in Three-Dimensional System

Our previous work has shown that prostate carcinoma cells exposed to media conditioned by marrow adipocytes take up the fat cell-supplied lipids in a process that results in accelerated tumor growth and invasiveness (5). Prostate tumor cells have been reported to be attracted to adipocyte-rich areas of the bone marrow, where metastases commonly occur (54, 55), and the translocation of adipocyte-stored lipids has been linked to increased tumor cell motility (56). The mechanisms behind the tumor cell attraction to marrow fat cells, however, remain poorly understood. Here, we have designed a simple 3D system in which tumor cell invasion toward adipocytes can be easily visualized, measured, and quantified (**Figure 7A**). We used collagen I, the predominant component of bone, as the matrix separating BODIPY-labeled bone marrow adipocytes (bottom) and DsRedexpressing PC3 cells (top). Tumor cells invaded through collagen I matrix over the course of 96 h (**Figures 7B–E**), with complete merging of the two cell types at the 96-h time point.

We next tested two compounds with potential to affect adipocyte–tumor cell interactions: a selective inhibitor of fatty acidbinding protein 4 (FABP4) (BMS309403) (5), and Atglistatin, a selective inhibitor of Adipocyte Triglyceride LIpase (ATGL) (57). We chose to inhibit FABP4 based on our previous studies demonstrating upregulation of this lipid transporter in prostate tumor cells exposed to marrow adipocytes, and the apparent efficacy of BMS309403 in inhibiting prostate tumor cell invasion in the 2D Boyden chamber assay (5). We also used Atglistatin as the means of inhibiting adipocyte-driven lipolysis, which we have shown to be induced by prostate tumor cells (see footnote text 1). As expected, under control conditions, the distance between PC3 cells and marrow adipocytes was significantly reduced after 70 h, compared with the baseline at 4 h (**Figures 8A,B**). Atglistatin alone did not seem to have any effect on tumor cell invasion toward the adipocytes (**Figure 8C**). Despite potent inhibition of PC3 invasion in 2D (5), only modest inhibition of tumor invasion was observed with FABP4 inhibitor in a 3D assay (**Figure 8D**), a result potentially due to differences in matrix and experimental conditions between the two assays. Notably, however, the use of both inhibitors in combination significantly inhibited tumor invasion and restored the distance between the two cell types to the levels measured at 4 h (**Figures 8E,F**). This suggests a potential link between lipid hydrolysis and transport between adipocytes and tumor cells that needs further investigation.

## DISCUSSION

The functional role of bone marrow adipocytes in the growth and aggressiveness of skeletal tumors is an understudied and not well-understood area of cancer research. There is a great need for *in vitro* 3D models that can address the complexity of the bone tumor microenvironment, yet allow dissection of the mechanisms behind the contribution of specific cell types to the metastatic growth. There has been a growing effort to generate 3D models that would represent a bridge between the 2D monolayer cultures and *in vivo* tumor microenvironment, with the idea of capturing the physiological complexity, multiplicity of cell types, ECM composition, and temporal and spatial distribution of soluble factors in the bone microenvironment (21, 28, 58–61). Many of these models have been key to demonstrating the importance of 3D tumor architecture in mechanistic understanding of basic cancer biology and, especially, in evaluating tumor response to therapy (22, 58, 61). Regretfully, however, none of these models to date have addressed the contribution of fat cells, one of most abundant cell types in the adult bone marrow to growth, phenotype, and behavior of metastatic tumor cells in bone.

The *in vitro* approaches we describe herein provide a simple way of examining interactions between bone marrow adipocytes and metastatic tumor cells in a physiologically relevant manner. Our Transwell system combines 3D culture of bone marrow-derived adipocytes with 3D culture of prostate tumor cells to allow paracrine interactions and sharing of nutrients and secreted factors by the two cell types. We show via immunofluorescence analysis of metabolism-associated proteins that under 3D conditions tumor cells have significantly different metabolic responses to adipocytes than tumor cells grown in a monolayer culture. This underlines the importance of employing 3D culture conditions to mimic physiological interactions between marrow adipocytes and metastatic tumor cells. It is noteworthy that the simple design of our 3D system allows easy manipulation, processing, and analysis of the resulting cultures. Because tumor cell cultures are established on glass coverslips they are immediately available for immunocytochemistry and confocal imaging. These samples can also be recovered from the coverslips and processed for protein and RNA analyses (data not shown), allowing for additional data to support the confocal imaging results. We have also shown the suitability of this system to image proteolysis by living cells. We used DQ Collagen IV as a representative component of the basement membrane, but this approach can be easily modified for use with other DQ-labeled matrices, such as DQ Collagen I [a major protein in the bone (33, 62)], or with activity-based probes (63).

Contribution of other bone marrow-derived cell types is an important consideration in the design of physiologically relevant cell culture models. Our data show that BMMs can be easily incorporated into our coculture system. More importantly, we reveal that macrophages are attracted to tumor spheroids exposed to adipocyte-derived factors, and their coculture with tumor cells results in a more aggressive morphology and an enhanced ECM proteolysis by both cell types. This speaks to the importance of multi-cellular design in modeling the effects of specific cell types on tumor phenotype and behavior. Our system allows the manipulation of the design to vary cell types, numbers, and ECM components. Studies are currently ongoing to examine the effects of direct interaction of marrow macrophages with adipocytes on tumor cell growth and aggressiveness.

Previous studies have indicated that prostate tumor cells are attracted to adipocyte-rich areas in the bone marrow (54, 55). Our 3D invasion assay results confirm this attraction and provide a simple approach to evaluate potential targets involved in this process. We utilized our invasion system to inhibit the lipid transporter FABP4, and the main adipocyte lipase ATGL and revealed that ATGL-driven lipolysis and FABP4-driven lipid transport might be important for tumor cell–adipocyte interaction in the bone marrow microenvironment. This not only opens new avenues for investigation but also speaks to the potential utility of this assay as an initial screen to identify factors behind tumor cell invasiveness toward the marrow adipocytes. Many G proteincoupled receptors (GPRCs), including chemokine receptors and receptors of bioactive lipids, have been implicated in tumor cell growth and invasiveness (64). Given the fact that adipocytes are an abundant source of chemokines and lipids, their role in modulating the invasive capacity of tumor cells via GPCRs could be studied in this system. It is also important to mention that this approach is easily modifiable in terms of matrix type and thickness, time of invasion and cell types used, representing a useful

## REFERENCES

tool to examine the functional effects of marrow adipocytes on tumor cell invasiveness.

We acknowledge that our models have limitations. First, we are utilizing adipocytes differentiated from bone marrow mesenchymal cells, which may not be entirely representative of adipocyte population in bone. Emerging data now show that there might be differences in bone marrow fat depending on localization in the bone (17), and we would not be able to capture these differences with our model. Second, we are utilizing murine bone marrow cells in combination with human tumor cells, which may not be entirely clinically relevant. Species-specific osteotropism is an important factor to be considered in design of physiologically relevant models (65). This would be particularly important when utilizing this model to study the effects of adipocyte-derived cytokines and chemokines on tumor cells. Adipokines, such as hepatocyte growth factor (HGF) or interleukin-6 (IL-6) are known to activate their receptors in a species-specific manner (66, 67). Nevertheless, it is important to mention that murine cells in our system can be easily replaced with human derived mesenchymal bone marrow cells to eliminate this issue.

Our model does not entirely recapitulate the bone tumor microenvironment found in the patient with metastatic disease, but it provides a controllable, testable system for examination of the molecular mechanisms behind adipocyte involvement in tumor progression in bone. We believe that the models described herein provide a good compromise between currently utilized 2D adipocyte-tumor cell cocultures and *in vivo* mouse models, where dissection of the specific contribution of adipocytes is not possible. Further development of these *in vitro* culture systems and combining them with other bone metastasis-focused cell culture models will open new strategies to increase our understanding of the role of marrow adipose tissue in metastatic progression.

## AUTHOR CONTRIBUTIONS

MH carried out most of the data acquisition and analyses and participated in writing of the manuscript. JD carried out some data acquisition and participated in data analyses and writing of the manuscript. IP wrote the manuscript and participated in some of the data acquisition and analyses.

## ACKNOWLEDGMENTS

The authors thank Dr. Kamiar Moin and the Microscopy, Imaging and Cytometry Resources Core (MICR) for assistance with confocal microscopy analyses. Grant support was provided by NIH/ NCI 1 R01 CA181189-01 (IP, PI), NIH 5T32CA009531-29 (JD, trainee), and P30 CA 22453 (MICR).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2016 Herroon, Diedrich and Podgorski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*