Following the definition of biotechnology, I shall delineate four areas of biotechnology that are undergoing explosive growth: gene editing and delivery; nanoparticulate delivery of drugs-prepared from natural polymers, low-cost metallic nanoparticles, and cellular-derived nanoparticles-exosomes; modeling in biotechnology and systems biology and artificial intelligence; and immunotherapy, particularly of cancer. First, I will mention some classical gene delivery methods, then cover in some detail relatively new development, the CRISPR-Cas9 method, dealing with gene editing and delivery. Even though this method matured only in 2002, exponential growth is noted. Some warning is mentioned because of flagrant se of this method in connection with the human embryo. The second part will deal with nanoparticles, covering three subjects. First, some polymeric nanoparticles will be introduced, followed by metallic-based nanoparticles prepared from bacteria, yeasts, algae, and plants with help of reducing agents. Some examples of what these nanoparticles can do will be listed, particularly of oxidation of toxic agents. Then a special nanoparticle introduced from all kinds of cells will be discussed-exosomes. This is is largely unexplored and will perhaps play a significant role in biotechnology. The third section will deal with modeling in biotechnology, with an emphasis on systems biology and closed with an exposure to the artificial intelligence which is going to play in biotechnology (is already playing in the automobile industry) significant growth in biotechnology once more molecular knowledge is accumulated. At last, medical biotechnology/immunology will be (it is already) more significant in fighting cancer. Immunology is undergoing revolution only now as the accumulated knowledge is now coming to fruition. We only mention a small portion of this burgeoning field, checkpoint inhibitors which are becoming more effective compared to the rest of drugs, fighting cancer

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied “the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer”. It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems–biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

There are significant engineering challenges in translating the remarkable medical implications of gene and nucleic acid delivery from cell and animal models into the clinic. Off-target effects and inefficient delivery to the proper intracellular compartment of the targeted cells are major obstacles to success. Systemic delivery of any viral or nonviral vector requires an appreciation of the adverse physiological barriers that exist in vivo and incorporation of well-designed vector components that limit non-specific uptake and accelerated clearance. In addition, the genetic engineer should consider the design criteria related to cell-specific action. For example, cell surface recognition can increase the therapeutic index of a DNA- or RNA-based medication. Finally, one must consider the mechanism of cellular internalization, and investigators should attempt to target pathways and incorporate vector functionalities that will mediate trafficking to the subcellular compartment that is optimal for activity of the nucleic acid cargo. This chapter discusses key aspects of biodistribution and cellular uptake of nanoparticulate vectors and vehicles. It also surveys current strategies for engineering effective viral and nonviral packaging systems, systems for both targeted, systemic delivery and controlled, local release of nucleic acids or genes from engineered scaffolds, and strategies for directing the intracellular trafficking of vehicle contents to the nucleus or cytoplasm of target cells.

Chemical, physical and mechanical methods of nanomaterial preparation are still regarded as mainstream methods, and the scientific community continues to search for new ways of nanomaterial preparation. The major objective of this review is to highlight the advantages of using green chemistry and bionanotechnology in the preparation of functional low-cost catalysts. Bionanotechnology employs biological principles and processes connected with bio-phase participation in both design and development of nano-structures and nano-materials, and the biosynthesis of metallic nanoparticles is becoming even more popular due to; (i) economic and ecologic effectiveness, (ii) simple one-step nanoparticle formation, stabilisation and biomass support and (iii) the possibility of bio-waste valorisation. Although it is quite difficult to determine the precise mechanisms in particular biosynthesis and research is performed with some risk in all trial and error experiments, there is also the incentive of understanding the exact mechanisms involved. This enables further optimisation of bionanoparticle preparation and increases their application potential. Moreover, it is very important in bionanotechnological procedures to ensure repeatability of the methods related to the recognised reaction mechanisms. This review, therefore, summarises the current state of nanoparticle biosynthesis. It then demonstrates the application of biosynthesised metallic nanoparticles in heterogeneous catalysis by identifying the many examples where bionanocatalysts have been successfully applied in model reactions. These describe the degradation of organic dyes, the reduction of aromatic nitro compounds, dehalogenation of chlorinated aromatic compounds, reduction of Cr(VI) and the synthesis of important commercial chemicals. To ensure sustainability, it is important to focus on nanomaterials that are capable of maintaining the important green chemistry principles directly from design inception to ultimate application.

In this work we have further developed the Direct Computer Mapping (DCM) based modelling and simulation methodology. A unified, transition-based representation of complex rule, reaction and influence networks has been introduced and two prototypes (one general state- and another general transition-prototype) have been developed for the unified functional modelling of the state and transition nodes. Starting from the network and from the functional prototypes, an automatic generation method of the graphically editable and extensible GraphML description of biosystem models has been elaborated. The new developments have been implemented in the improved kernel of DCM models. The applied knowledge representation makes possible the unified generation and execution of the balance-based quantitative and influence- or rule-based qualitative, as well as optionally time-driven, multiscale biosystem models. Application of the developed methodology has been illustrated by the improved implementation of the formerly studied and upgraded example biosystem model for combining the detailed, quantitative p53/miR34a signalling system with the pathological model through an extended rule-based coupling model.

A critical review is attempted to assess the status of nanomedicine entry onto the market. The emergence of new potential therapeutic entities such as DNA and RNA fragments requires that these new "drugs" will need to be delivered in a cell-and organelle-specific manner. Although efforts have been made over the last 50 years or so to develop such delivery technology, no effective and above all clinically approved protocol for cell-specific drug delivery in humans exists as yet. Various particles, macromolecules, liposomes and most recently "nanomaterials" have been said to "show promise" but none of these promises have so far been "reduced" to human clinical practice.The focus of this volume is on cancer indication since the majority of published research relates to this application; within that, we focus on solid tumors (solid malignancies). Our aim is to critically evaluate whether nanomaterials, both non-targeted and targeted to specific cells, could be of therapeutic benefit in clinical practice. The emphasis of this volume will be on pharmacokinetics (PK) and pharmacodynamics (PD) in animal and human studies.Apart from the case of exquisitely specific antibody-based drugs, the development of target-specific drug-carrier delivery systems has not yet been broadly successful at the clinical level. It can be argued that drugs generated using the conventional means of drug development (i.e., relying on facile biodistribution and activity after (preferably) oral administration) are not suitable for a target-specific delivery and would not benefit from such delivery even when a seemingly perfect delivery system is available. Therefore, successful development of site-selective drug delivery systems will need to include not only the development of suitable carriers, but also the development of drug entities that meet the required PK/PD profile.In general, human clinical studies are approved only after the expected benefits of targeting have been shown in pre-clinical, in vivo animal studies first. Therefore, quantitative data on biodistribution of targeted and non-targeted nanoparticles should be generated as the first step. This should be followed by determining whether an increased presence of nanoparticles in tumors also results in increased concentration of the free drug within the tumor space. Any "promise" for reproducing similar data in human clinical studies should be supported by relevant scaling from the animal model used to humans.For too long now, the same or similar approaches have been used by researchers without success. We believe that new fundamentally different approaches are needed to make cell-specific drug delivery clinical reality. In this volume we want to focus on (a) how nanoparticles could be redesigned from the material-science point of view (for example, redesigning nanoparticles for long-circulating properties, passive (EPR) and active targeting concept); and (b) on the design and properties of drugs that would benefit from cell-specific targeting (examining why active targeting of drug carrier does not necessarily result in drug accumulation in tumor). Further, we will draw attention to (c) the manner pre-clinical animal data should be translated to humans using appropriate scaling, in particular with reference to the differences between mice and men in terms of differing vascular morphology and immunological background.Successful development of cell-specific drug-delivery systems requires that reliable quantitative pharmacokinetic/pharmacodynamic (PK/PD) data are collected both in animal and human studies. This volume will include (d) information on improved body imaging technologies and on enabling quantitative tools available.Finally, we address (e) the issue of diminishing academic funding of animal studies and of (f) the current dismal market and proprietary situation in the area of site-specific drug delivery.

Growth in the pharmaceutical market has slowed down almost to a standstill. One reason is that governments and other payers are cutting costs in a faltering world economy. But a more fundamental problem is the failure of major companies to discover, develop and market new drugs. Major drugs losing patent protection or being withdrawn from the market are simply not being replaced by new therapies the pharmaceutical market model is no longer functioning effectively and most pharmaceutical companies are failing to produce the innovation needed for success. This multi-authored new book looks at a vital strategy which can bring innovation to a market in need of new ideas and new products: Systems Biology (SB). Modeling is a significant task of systems biology. SB aims to develop and use efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of biological systems, such as the networks of metabolites comprise signal transduction pathways and gene regulatory networks to both analyze and visualize the complex connections of these cellular processes. SB involves a series of operational protocols used for performing research, namely a cycle composed of theoretical, analytic or computational modeling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.

In the face of the challenges associated with expiring drug patents, the rising cost of R&D, and payer pressure on pricing, most major pharmaceutical companies are seeking ways to enhance productivity, reduce costs and augment their late-stage pipelines. Recent technological applications have witnessed the development of data-rich, genome-scale functional screens, large collections of reagents and protein microarrays, and the addition of databases and algorithms for data mining. Systems biology takes advantage of these technological breakthroughs to represent a combination of reductionist and holistic approaches to the relationships among the elements of a system.

This chapter covers qualitative in vivo approaches in animals and man, which will help to develop in silico pharmacology and PK positions. Additionally, we cover RNA interference in this chapter even though it is largely an in vitro method for characterizing the dynamics of cell physiology. And though in silico pharmacology is only in a rudimentary state, it is vitally important for clinical model based drug design (MBDD) development (see Chap. 10).

In order to cover bottom-up and top-down phenomena multiscale SB simulation tools should include organ-level considerations, and should be used in conjunction with multiscale modeling tools which have the ability to handle many orders of magnitude in both length and timescale. Several new R&D paradigms, based on CSB, are proposed, while some are already in the research stage. This effort will lead to virtual organ/disease models, emerging as important tools. Identifying and targeting a system’s emergent properties is a major goal for coming years. This will cause a paradigm shift in R&D activity in Pharma yielding a move from population models to models of individualized medicine. The importance of multiscale CSB is underlined here as a great attention is given here in this section.

To date, few cellular and gene networks have been reconstructed and analyzed in full. Examples include some prokaryotes and few eukaryotes for cellular networks. The methods currently used to analyze single database genomic sets are usually mature and refined. Network reconstruction is also enabled by analysing the molecular connectivity of a system by using correlation analysis. Additionally, monitoring the dynamics of the system and measuring the system’s responses to perturbations such as drug administration or challenge tests can yield insights into the dynamics of the system. Microbial cells are fairly well characterized, but the status of similar efforts for mammalian cells is rather poor. While emergence can be conveniently studied via computational tools, the phenomenon of emergence is the single most important benefit of CSB.

SB is important for DD because it can be used to rapidly identify the MoA of novel drugs, enabling companies to make go/no decisions earlier in the drug development process by avoiding pathways associated with toxicological or pharmacological issues. SB can reduce the number of compounds synthesized and manufactured owing to refined algorithms which avoid poor PK and toxic effects. In the longer term investments in SB will enable research institutions and companies to save time and money in the DD process by choosing drugs which are more likely to succeed in clinical development.

Drug formulation and delivery is a rapidly developing field that is also a very mature area of the R&D process. Novel methods continue to contribute to improved modalities. The whole landscape will change very soon and it will have a key role to play in replacing existing drugs with expiring patents. Nanoscale delivery methods are in rapid development, and will allow for efficient cell internalization and enhanced efficacy.

In our introduction, we emphasized that a combination of reductionist (mechanism-based) and holistic (hypothesis-based) tools in the drug screening process may increase the efficiency of overall Drug Discovery. Among notable holistic tools are screens that target discovery and characterization of molecular probes (compounds) that will enable the investigation of fundamental biological function at molecular, cellular and whole organism levels. Such screening usually occurs at the earlier stages of drug discovery.

Large scale in silico clinical development will only become a reality after some effort is exerted; some partial solutions (SB and BI tools mentioned in previous chapters) already exist and first attempts have been made. We acknowledge here that we are NOT in square zero and that in silico technologies have been, and are already being, used in clinical trial design and execution, used in simulation studies for adaptive trials, and used to identify patient subpopulations and markers for enrichment strategies. This goal will require a concentrated effort by all players, with considerable investment from the pharma industry and governments.

In silico PKPD/ADMET and biochemical-mechanistic methods will become a standard approach in the coming few years via the employment of BI and SB tools at the multiscale whole-body level. So far, the overall impact of toxicity markers on preclinical safety testing has been modest. The greatest benefit of PBPK models is they may allow for individualized health care.